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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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• Original Research
• Open access
• Published: 30 January 2018

Teaching and learning mathematics through error analysis

• Sheryl J. Rushton 1  

Fields Mathematics Education Journal volume  3 , Article number:  4 ( 2018 ) Cite this article

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For decades, mathematics education pedagogy has relied most heavily on teachers, demonstrating correctly worked example exercises as models for students to follow while practicing their own exercises. In more recent years, incorrect exercises have been introduced for the purpose of student-conducted error analysis. Combining the use of correctly worked exercises with error analysis has led researchers to posit increased mathematical understanding. Combining the use of correctly worked exercises with error analysis has led researchers to posit increased mathematical understanding.

A mixed method design was used to investigate the use of error analysis in a seventh-grade mathematics unit on equations and inequalities. Quantitative data were used to establish statistical significance of the effectiveness of using error analysis and qualitative methods were used to understand participants’ experience with error analysis.

The results determined that there was no significant difference in posttest scores. However, there was a significant difference in delayed posttest scores.

In general, the teacher and students found the use of error analysis to be beneficial in the learning process.

For decades, mathematics education pedagogy has relied most heavily on teachers demonstrating correctly worked example exercises as models for students to follow while practicing their own exercises [ 3 ]. In more recent years, incorrect exercises have been introduced for the purpose of student-conducted error analysis [ 17 ]. Conducting error analysis aligns with the Standards of Mathematical Practice [ 18 , 19 ] and the Mathematics Teaching Practices [ 18 ]. Researchers posit a result of increased mathematical understanding when these practices are used with a combination of correctly and erroneously worked exercises [ 1 , 4 , 8 , 11 , 15 , 16 , 18 , 19 , 23 ].

Review of literature

Correctly worked examples consist of a problem statement with the steps taken to reach a solution along with the final result and are an effective method for the initial acquisitions of procedural skills and knowledge [ 1 , 11 , 26 ]. Cognitive load theory [ 1 , 11 , 25 ] explains the challenge of stimulating the cognitive process without overloading the student with too much essential and extraneous information that will limit the working memory and leave a restricted capacity for learning. Correctly worked examples focus the student’s attention on the correct solution procedure which helps to avoid the need to search their prior knowledge for solution methods. Correctly worked examples free the students from performance demands and allow them to concentrate on gaining new knowledge [ 1 , 11 , 16 ].

Error analysis is an instructional strategy that holds promise of helping students to retain their learning [ 16 ]. Error analysis consists of being presented a problem statement with the steps taken to reach a solution in which one or more of the steps are incorrect, often called erroneous examples [ 17 ]. Students analyze and explain the errors and then complete the exercise correctly providing reasoning for their own solution. Error analysis leads students to enact two Standards of Mathematical Practice, namely, (a) make sense of problems and persevere in solving them and (b) attend to precision [ 19 ].

Another of the Standards of Mathematical Practice suggests that students learn to construct viable arguments and comment on the reasoning of others [ 19 ]. According to Große and Renkl [ 11 ], students who attempted to establish a rationale for the steps of the solution learned more than those who did not search for an explanation. Teachers can assist in this practice by facilitating meaningful mathematical discourse [ 18 ]. “Arguments do not have to be lengthy, they simply need to be clear, specific, and contain data or reasoning to back up the thinking” [ 20 ]. Those data and reasons could be in the form of charts, diagrams, tables, drawings, examples, or word explanations.

Researchers [ 7 , 21 ] found the process of explaining and justifying solutions for both correct and erroneous examples to be more beneficial for achieving learning outcomes than explaining and justifying solutions to correctly worked examples only. They also found that explaining why an exercise is correct or incorrect fostered transfer and led to better learning outcomes than explaining correct solutions only. According to Silver et al. [ 22 ], students are able to form understanding by classifying procedures into categories of correct examples and erroneous examples. The students then test their initial categories against further correct and erroneous examples to finally generate a set of attributes that defines the concept. Exposing students to both correctly worked examples and error analysis is especially beneficial when a mathematical concept is often done incorrectly or is easily confused [ 11 ].

Große and Renkl [ 11 ] suggested in their study involving university students in Germany that since errors are inherent in human life, introducing errors in the learning process encourages students to reflect on what they know and then be able to create clearer and more complete explanations of the solutions. The presentation of “incorrect knowledge can induce cognitive conflicts which prompt the learner to build up a coherent knowledge structure” [ 11 ]. Presenting a cognitive conflict through erroneously worked exercises triggers learning episodes through reflection and explanations, which leads to deeper understanding [ 29 ]. Error analysis “can foster a deeper and more complete understanding of mathematical content, as well as of the nature of mathematics itself” [ 4 ].

Several studies have been conducted on the use of error analysis in mathematical units [ 1 , 16 , 17 ]. The study conducted for this article differed from these previous studies in mathematical content, number of teachers and students involved in the study, and their use of a computer or online component. The most impactful differences between the error analysis studies conducted in the past and this article’s study are the length of time between the posttest and the delayed posttest and the use of qualitative data to add depth to the findings. The previous studies found students who conducted error analysis work did not perform significantly different on the posttest than students who received a more traditional approach to learning mathematics. However, the students who conducted error analysis outperformed the control group in each of the studies on delayed posttests that were given 1–2 weeks after the initial posttest.

Loibl and Rummel [ 15 ] discovered that high school students became aware of their knowledge gaps in a general manner by attempting an exercise and failing. Instruction comparing the erroneous work with correctly worked exercises filled the learning gaps. Gadgil et al. [ 9 ] conducted a study in which students who compared flawed work to expertly done work were more likely to repair their own errors than students who only explained the expertly done work. This discovery was further supported by other researchers [8, 14, 24]. Each of these researchers found students ranging from elementary mathematics to university undergraduate medical school who, when given correctly worked examples and erroneous examples, learned more than students who only examined correctly worked examples. This was especially true when the erroneous examples were similar to the kinds of errors that they had committed [ 14 ]. Stark et al. [ 24 ] added that it is important for students to receive sufficient scaffolding in correctly worked examples before and alongside of the erroneous examples.

The purpose of this study was to explore whether seventh-grade mathematics students could learn better from the use of both correctly worked examples and error analysis than from the more traditional instructional approach of solving their exercises in which the students are instructed with only correctly worked examples. The study furthered previous research on the subject of learning from the use of both correctly worked examples and error analysis by also investigating the feedback from the teacher’s and students’ experiences with error analysis. The following questions were answered in this study:

What was the difference in mathematical achievement when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only?

What kind of benefits or disadvantages did the students and teacher observe when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only?

A mixed method design was used to investigate the use of error analysis in a seventh-grade mathematics unit on equations and inequalities. Quantitative data were used to establish statistical significance of the effectiveness of using error analysis and qualitative methods were used to understand participants’ experience with error analysis [ 6 , 27 ].

Participants

Two-seventh-grade mathematics classes at an International Baccalaureate (IB) school in a suburban charter school in Northern Utah made up the control and treatment groups using a convenience grouping. One class of 26 students was the control group and one class of 27 students was the treatment group.

The same teacher taught both the groups, so a comparison could be made from the teacher’s point of view of how the students learned and participated in the two different groups. At the beginning of the study, the teacher was willing to give error analysis a try in her classroom; however, she was not enthusiastic about using this strategy. She could not visualize how error analysis could work on a daily basis. By the end of the study, the teacher became very enthusiastic about using error analysis in her seventh grade mathematics classes.

The total group of participants involved 29 males and 24 females. About 92% of the participants were Caucasian and the other 8% were of varying ethnicities. Seventeen percent of the student body was on free or reduced lunch. Approximately 10% of the students had individual education plans (IEP).

A pretest and posttest were created to contain questions that would test for mathematical understanding on equations and inequalities using Glencoe Math: Your Common Core Edition CCSS [ 5 ] as a resource. The pretest was reused as the delayed posttest. Homework assignments were created for both the control group and the treatment group from the Glencoe Math: Your Common Core Edition CCSS textbook. However, the researcher rewrote two to three of the homework exercises as erroneous examples for the treatment group to find the error and fix the exercise with justifications (see Figs.  1 , 2 ). Students from both groups used an Assignment Time Log to track the amount of time which they spent on their homework assignments.

Example of the rewritten homework exercises as equation erroneous examples

Example of the rewritten homework exercises as inequality erroneous examples

Both the control and the treatment groups were given the same pretest for an equations and inequality unit. The teacher taught both the control and treatment groups the information for the new concepts in the same manner. The majority of the instruction was done using the direct instruction strategy. The students in both groups were allowed to work collaboratively in pairs or small groups to complete the assignments after instruction had been given. During the time she allotted for answering questions from the previous assignment, she would only show the control group the exercises worked correctly. However, for the treatment group, the teacher would write errors which she found in the students’ work on the board. She would then either pair up the students or create small groups and have the student discuss what errors they noticed and how they would fix them. Often, the teacher brought the class together as a whole to discuss what they discovered and how they could learn from it.

The treatment group was given a homework assignment with the same exercises as the control group, but including the erroneous examples. Students in both the control and treatment groups were given the Assignment Time Log to keep a record of how much time was spent completing each homework assignment.

At the end of each week, both groups took the same quiz. The quizzes for the control group received a grade, and the quiz was returned without any further attention. If a student asked how to do an exercise, the teacher only showed the correct example. The teacher graded the quizzes for the treatment group using the strategy found in the Teaching Channel’s video “Highlighting Mistakes: A Grading Strategy” [ 2 ]. She marked the quizzes by highlighting the mistakes; no score was given. The students were allowed time in class or at home to make corrections with justifications.

The same posttest was administered to both groups at the conclusion of the equation and inequality chapter, and a delayed posttest was administered 6 weeks later. The delayed posttest also asked the students in the treatment group to respond to an open-ended request to “Please provide some feedback on your experience”. The test scores were analyzed for significant differences using independent samples t tests. The responses to the open-ended request were coded and analyzed for similarities and differences, and then, used to determine the students’ perceptions of the benefits or disadvantages of using error analysis in their learning.

At the conclusion of gathering data from the assessments, the researcher interviewed the teacher to determine the differences which the teacher observed in the preparation of the lessons and students’ participation in the lessons [ 6 ]. The interview with the teacher contained a variety of open-ended questions. These are the questions asked during the interview: (a) what is your opinion of using error analysis in your classroom at the conclusion of the study versus before the study began? (b) describe a typical classroom discussion in both the control group class and the treatment group class, (c) talk about the amount of time you spent grading, preparing, and teaching both groups, and (d) describe the benefits or disadvantages of using error analysis on a daily basis compared to not using error analysis in the classroom. The responses from the teacher were entered into a computer, coded, and analyzed for thematic content [ 6 , 27 ]. The themes that emerged from coding the teacher’s responses were used to determine the kind of benefits or disadvantages observed when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only from the teacher’s point of view.

Findings and discussion

Mathematical achievement.

Preliminary analyses were carried out to evaluate assumptions for the t test. Those assumptions include: (a) the independence, (b) normality tested using the Shapiro–Wilk test, and (c) homogeneity of variance tested using the Levene Statistic. All assumptions were met.

The Levene Statistic for the pretest scores ( p  > 0.05) indicated that there was not a significant difference in the groups. Independent samples t tests were conducted to determine the effect error analysis had on student achievement determined by the difference in the means of the pretest and posttest and of the pretest and delayed posttest. There was no significant difference in the scores from the posttest for the control group ( M  = 8.23, SD = 5.67) and the treatment group ( M  = 9.56, SD = 5.24); t (51) = 0.88, p  = 0.381. However, there was a significant difference in the scores from the delayed posttest for the control group ( M  = 5.96, SD = 4.90) and the treatment group ( M  = 9.41, SD = 4.77); t (51) = 2.60, p  = 0.012. These results suggest that students can initially learn mathematical concepts through a variety of methods. Nevertheless, the retention of the mathematical knowledge is significantly increased when error analysis is added to the students’ lessons, assignments, and quizzes. It is interesting to note that the difference between the means from the pretest to the posttest was higher in the treatment group ( M  = 9.56) versus the control group ( M  = 8.23), implying that even though there was not a significant difference in the means, the treatment group did show a greater improvement.

The Assignment Time Log was completed by only 19% of the students in the treatment group and 38% of the students in the control group. By having such a small percentage of each group participate in tracking the time spent completing homework assignment, the results from the t test analysis cannot be used in any generalization. However, the results from the analysis were interesting. The mean time spent doing the assignments for each group was calculated and analyzed using an independent samples t test. There was no significant difference in the amount of time students which spent on their homework for the control group ( M  = 168.30, SD = 77.41) and the treatment group ( M  = 165.80, SD = 26.53); t (13) = 0.07, p  = 0.946. These results suggest that the amount of time that students spent on their homework was close to the same whether they had to do error analyses (find the errors, fix them, and justify the steps taken) or solve each exercise in a traditional manner of following correctly worked examples. Although the students did not spend a significantly different amount of time outside of class doing homework, the treatment group did spend more time during class working on quiz corrections and discussing error which could attribute to the retention of knowledge.

Feedback from participants

All students participating in the current study submitted a signed informed consent form. Students process mathematical procedures better when they are aware of their own errors and knowledge gaps [ 15 ]. The theoretical model of using errors that students make themselves and errors that are likely due to the typical knowledge gaps can also be found in works by other researchers such as Kawasaki [ 14 ] and VanLehn [ 29 ]. Highlighting errors in the students’ own work and in typical errors made by others allowed the participants in the treatment group the opportunity to experience this theoretical model. From their experiences, the participants were able to give feedback to help the researcher delve deeper into what the thoughts were of the use of error analysis in their mathematics classes than any other study provided [ 1 , 4 , 7 , 8 , 9 , 11 , 14 , 15 , 16 , 17 , 21 , 23 , 24 , 25 , 26 , 29 ]. Overall, the teacher and students found the use of error analysis in the equations and inequalities unit to be beneficial. The teacher pointed out that the discussions in class were deeper in the treatment group’s class. When she tried to facilitate meaningful mathematical discourse [ 18 ] in the control group class, the students were unable to get to the same level of critical thinking as the treatment group discussions. In the open-ended question at the conclusion of the delayed posttest (“Please provide some feedback on your experience.”), the majority (86%) of the participants from the treatment group indicated that the use of erroneous examples integrated into their lessons was beneficial in helping them recognize their own mistakes and understanding how to correct those mistakes. One student reported, “I realized I was doing the same mistakes and now knew how to fix it”. Several (67%) of the students indicated learning through error analysis made the learning process easier for them. A student commented that “When I figure out the mistake then I understand the concept better, and how to do it, and how not to do it”.

When students find and correct the errors in exercises, while justifying themselves, they are being encouraged to learn to construct viable arguments and critique the reasoning of others [ 19 ]. This study found that explaining why an exercise is correct or incorrect fostered transfer and led to better learning outcomes than explaining correct solutions only. However, some of the higher level students struggled with the explanation component. According to the teacher, many of these higher level students who typically do very well on the homework and quizzes scored lower on the unit quizzes and tests than the students expected due to the requirement of explaining the work. In the past, these students had not been justifying their thinking and always got correct answers. Therefore, providing reasons for erroneous examples and justifying their own process were difficult for them.

Often teachers are resistant to the idea of using error analysis in their classroom. Some feel creating erroneous examples and highlighting errors for students to analyze is too time-consuming [ 28 ]. The teacher in this study taught both the control and treatment groups, which allowed her the perspective to compare both methods. She stated, “Grading took about the same amount of time whether I gave a score or just highlighted the mistakes”. She noticed that having the students work on their errors from the quizzes and having them find the errors in the assignments and on the board during class time ultimately meant less work for her and more work for the students.

Another reason behind the reluctance to use error analysis is the fact that teachers are uncertain about exposing errors to their students. They are fearful that the discussion of errors could lead their students to make those same errors and obtain incorrect solutions [ 28 ]. Yet, most of the students’ feedback stated the discussions in class and the error analyses on the assignments and quizzes helped them in working homework exercises correctly. Specifically, they said figuring out what went wrong in the exercise helped them solve that and other exercises. One student said that error analysis helped them “do better in math on the test, and I actually enjoyed it”. Nevertheless, 2 of the 27 participating students in the treatment group had negative comments about learning through error analysis. One student did not feel that correcting mistakes showed them anything, and it did not reinforce the lesson. The other student stated being exposed to error analysis did, indeed, confuse them. The student kept thinking the erroneous example was a correct answer and was unsure about what they were supposed to do to solve the exercise.

When the researcher asked the teacher if there were any benefits or disadvantages to using error analysis in teaching the equations and inequalities unit, she said that she thoroughly enjoyed teaching using the error analysis method and was planning to implement it in all of her classes in the future. In fact, she found that her “hands were tied” while grading the control group quizzes and facilitating the lessons. She said, “I wanted to have the students find their errors and fix them, so we could have a discussion about what they were doing wrong”. The students also found error analysis to have more benefits than disadvantages. Other than one student whose response was eliminated for not being on topic and the two students with negative comments, the other 24 of the students in the treatment group had positive comments about their experience with error analysis. When students had the opportunity to analyze errors in worked exercises (error analysis) through the assignments and quizzes, they were able to get a deeper understanding of the content and, therefore, retained the information longer than those who only learned through correct examples.

Discussions generated in the treatment group’s classroom afforded the students the opportunity to critically reason through the work of others and to develop possible arguments on what had been done in the erroneous exercise and what approaches might be taken to successfully find a solution to the exercise. It may seem surprising that an error as simple as adding a number when it should have been subtracted could prompt a variety of questions and lead to the students suggesting possible ways to solve and check to see if the solution makes sense. In an erroneous exercise presented to the treatment group, the students were provided with the information that two of the three angles of a triangle were 35° and 45°. The task was to write and solve an equation to find the missing measure. The erroneous exercise solver had created the equation: x  + 35 + 45 = 180. Next was written x  + 80 = 180. The solution was x  = 260°. In the discussion, the class had on this exercise, the conclusion was made that the error occurred when 80 was added to 180 to get a sum of 260. However, the discussion progressed finding different equations and steps that could have been taken to discover the missing angle measure to be 100° and why 260° was an unreasonable solution. Another approach discussed by the students was to recognize that to say the missing angle measure was 260° contradicted with the fact that one angle could not be larger than the sum of the angle measures of a triangle. Analyzing the erroneous exercises gave the students the opportunity of engaging in the activity of “explaining” and “fixing” the errors of the presented exercise as well as their own errors, an activity that fostered the students’ learning.

The students participating in both the control and treatment groups from the two-seventh-grade mathematics classes at the IB school in a suburban charter school in Northern Utah initially learned the concepts taught in the equations and inequality unit statistically just as well with both methods of teaching. The control group had the information taught to them with the use of only correctly worked examples. If they had a question about an exercise which they did wrong, the teacher would show them how to do the exercise correctly and have a discussion on the steps required to obtain the correct solutions. On their assignments and quizzes, the control group was expected to complete the work by correctly solving the equations and inequalities in the exercise, get a score on their work, and move on to the next concept. On the other hand, the students participating in the treatment group were given erroneous examples within their assignments and asked to find the errors, explain what had been done wrong, and then correctly solve the exercise with justifications for the steps they chose to use. During lessons, the teacher put erroneous examples from the students’ work on the board and generated paired, small groups, or whole group discussion of what was wrong with the exercise and the different ways to do it correctly. On the quizzes, the teacher highlighted the errors and allowed the students to explain the errors and justify the correct solution.

Both the method of teaching using error analysis and the traditional method of presenting the exercise and having the students solve it proved to be just as successful on the immediate unit summative posttest. However, the delayed posttest given 6 weeks after the posttest showed that the retention of knowledge was significantly higher for the treatment group. It is important to note that the fact that the students in the treatment group were given more time to discuss the exercises in small groups and as a whole class could have influenced the retention of mathematical knowledge just as much or more than the treatment of using error analysis. Researchers have proven academic advantages of group work for students, in large part due to the perception of students having a secure support system, which cannot be obtained when working individually [ 10 , 12 , 13 ].

The findings of this study supported the statistical findings of other researchers [ 1 , 16 , 17 ], suggesting that error analysis may aid in providing a richer learning experience that leads to a deeper understanding of equations and inequalities for long-term knowledge. The findings of this study also investigated the teacher’s and students’ perceptions of using error analysis in their teaching and learning. The students and teacher used for this study were chosen to have the same teacher for both the control and treatment groups. Using the same teacher for both groups, the researcher was able to determine the teacher’s attitude toward the use of error analysis compared to the non-use of error analysis in her instruction. The teacher’s comments during the interview implied that she no longer had an unenthusiastic and skeptical attitude toward the use of error analysis on a daily basis in her classroom. She was “excited to implement the error analysis strategy into the rest of her classes for the rest of the school year”. She observed error analysis to be an effective way to deal with common misconceptions and offer opportunities for students to reflect on their learning from their errors. The process of error analysis assisted the teacher in supporting productive struggle in learning mathematics [ 18 ] and created opportunity for students to have deep discussions about alternative ways to solve exercises. Error analysis also aided in students’ discovery of their own errors and gave them possible ways to correct those errors. Learning through the use of error analysis was enjoyable for many of the participating students.

According to the NCTM [ 18 ], effective teaching of mathematics happens when a teacher implements exercises that will engage students in solving and discussing tasks that promote mathematical reasoning and problem solving. Providing erroneous examples allowed discussion, multiple entry points, and varied solution strategies. Both the teacher and the students participating in the treatment group came to the conclusion that error analysis is a beneficial strategy to use in the teaching and learning of mathematics. Regardless of the two negative student comments about error analysis not being helpful for them, this researcher recommends the use of error analysis in teaching and learning mathematics.

The implications of the treatment of teaching students mathematics through the use of error analysis are that students’ learning could be fostered and retention of content knowledge may be longer. When a teacher is able to have their students’ practice critiquing the reasoning of others and creating viable arguments [ 19 ] by analyzing errors in mathematics, the students not only are able to meet the Standard of Mathematical Practice, but are also creating a lifelong skill of analyzing the effectiveness of “plausible arguments, distinguish correct logic or reasoning from that which is flawed, and—if there is a flaw in an argument—explain what it is” ([ 19 ], p. 7).

Limitations and future research

This study had limitations. The sample size was small to use the same teacher for both groups. Another limitation was the length of the study only encompassed one unit. Using error analysis could have been a novelty and engaged the students more than it would when the novelty wore off. Still another limitation was the study that was conducted at an International Baccalaureate (IB) school in a suburban charter school in Northern Utah, which may limit the generalization of the findings and implications to other schools with different demographics.

This study did not have a separation of conceptual and procedural questions on the assessments. For a future study, the creation of an assessment that would be able to determine if error analysis was more helpful in teaching conceptual mathematics or procedural mathematics could be beneficial to teachers as they plan their lessons. Another suggestion for future research would be to gather more data using several teachers teaching both the treatment group and the control group.

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Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

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• Published: 18 April 2024

Effects of human milk odor stimulation on feeding in premature infants: a systematic review and meta-analysis

• Yangyang Qin 1 ,
• Shu Liu 1 ,
• Yanming Yang 1 ,
• Yuan Zhong 1 ,
• Danshi Hao 2 &
• Han Han 3  

Scientific Reports volume  14 , Article number:  8964 ( 2024 ) Cite this article

Metrics details

• Health care
• Medical research

Previous studies suggested odor stimulation may influence feeding of premature neonates. Therefore, this systematic review and meta-analysis of randomized controlled trials was conducted to assess the effect of human milk odor stimulation on feeding of premature infants. All randomized controlled trials related to human milk odor stimulation on feeding in premature infants published in PubMed, Cochrane, Library, Medline, Embase, Web of science databases and Chinese biomedical literature databases, China National Knowledge Infrastructure, China Science and Technology Journal Database (VIP) and Wanfang Chinese databases were searched, and The Cochrane Handbook 5.1.0 was used to evaluate the quality and authenticity of the literature. Relevant information of the included studies was extracted and summarized, and the evaluation indexes were analyzed using ReviewManager5.3. The retrieval time was from the establishment of the database to July 28, 2022.12 articles were assessed for eligibility, and six randomized controlled studies were eventually included in the meta-analysis (PRISMA). A total of 6 randomized controlled studies with 763 patients were finally included in the study, and the quality evaluation of literatures were all grade B. Human milk odor stimulation reduced the transition time to oral feeding in premature infants [SMD = − 0.48, 95% CI (− 0.69, − 0.27), Z = 4.54, P  < 0.00001] and shortened the duration of parenteral nutrition [MD = − 1.01, 95% CI (− 1.70, − 0.32), Z = 2.88, P  = 0.004]. However, it did not change the length of hospitalization for premature infants [MD = − 0.03, 95% CI (− 0.41, 0.35), Z = 0.17, P  = 0.86]. The implementation of human milk odor stimulation can reduce the transition time to oral feeding and the duration of parenteral nutrition in premature infants, but further studies are needed to determine whether it can reduce the length of hospital stay in premature infants. More high-quality, large-sample studies are needed to investigate the effect of human milk odor stimulation on the feeding process and other outcomes in premature infants.

Introduction

Premature infants are babies born alive before 37 weeks of pregnancy are completed. According to the relevant World Health Organization (WHO) report, 15 million premature infants are born every year in the world 1 . Preterm birth is an important public health issue, as it is associated to a high burden of mortality and morbidities 2 . Premature infants are at high risk for aspiration due to poor coordination of sucking and swallowing 3 . Thus, they usually need tube feeding for nutritional needs with a gradual transition to oral feeding. Premature infants need to start oral feeding at the youngest possible age to improve survival and recovery 4 .

It has been shown 5 , 6 , 7 that fetal olfactory receptors begin to appear in the 8th week of pregnancy, ciliated olfactory receptors mature in the 24th week, and the nasopharyngeal epithelium can express olfactory marker proteins in the 28th week. Premature infants, just like full-term infants, possess a more advanced olfactory system at birth, enabling them to detect, selectively process, retain, and recall odor information. They are able to distinguish between different odors, including those of human milk, even without history of postpartum exposure to such odors 8 , 9 . Olfactory stimulus refers to an environmental stimulus that uses a familiar odor or aromatic odor and is transmitted to the cerebral cortex through olfactory receptors and olfactory nerves to produce an olfactory response. In recent years, an increasing number of studies have used olfactory stimulation as a non-drug intervention to improve the effects of feeding. For example, human milk odor stimulation has a sedative effect on neonates 10 , 11 and relieve the pain 12 caused by venipuncture. The milk odor can also prevent apnea 13 and improve oxygen saturation 14 in premature infants.

Nutritional status parameters, including body weight and oral feeding, are key in determining whether the premature infants can be discharged in time. At present, many reports on the application of human milk odor stimulation to improve the nutritional status of premature infants, but there are differences in the research results and a lack of comprehensive evaluation. Therefore, this systematic review and meta-analysis was conducted to comprehensively evaluate the effect of human milk odor stimulation, and to provide updated evidence for the development of nursing measures in clinical practice.

This study was conducted in conformity to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 15 .

Search strategy

Literature search was conducted from the establishment of the database to July 2022. We searched PubMed/Medline, Cochrane, Library, Embase, Web of science , Chinese biomedical literature databases, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP ), and Wanfang Data Knowledge Service Platform to retrieve published studies on the human milk odor stimulation as an intervention to improve nutritional status in premature infants. Keyword selection and search included both medical subject headings (MeSH) and life science term indexes (EMBASE TREE; EMTREE). The relevant retrieval strategy was as follows: (“Infant newborn” OR “infant” OR “newborn” OR “neonate”) AND (“Feeding” OR “nutrition” OR “feed” OR “nourishment” OR “pabulum”) AND (“Olfactory stimulation” OR “breast milk stimulation” OR “olfactory” OR “human milk” OR “breast milk” OR “odorant” OR “odor” OR “odour” OR “smell”).

Inclusion criteria

Study characteristics used as criteria for eligibility are as follows: (1) Premature infants born at less than 37 weeks gestation who are receiving tube feeding and/or parenteral nutrition; (2) randomized controlled trials; (3) both groups of premature infants were given tube feeding and/or parenteral nutrition, with interventions involving the application of human milk odor stimulation in the intervention group and routine care in the control group; (4) evaluation metrics included transition time to oral feeding, length of stay, duration of parenteral nutrition, and/or body weight; (5) English or Chinese.

Exclusion criteria

(1) Duplicate articles; (2) Preclinical study, meta-analysis, case reports, reviews, guidelines; (3) Valid ending data unable to be extracted or calculated; (4) Full text of the study is not available; (5) The quality evaluation is grade C.

Data extraction

Two authors (YW and AP) carried out the data extraction process independently. Any disagreement was resolved with a senior researcher (CS) through discussion and consensus. Extracted contents were listed as follows: (1) Basic information of the included articles (title, the first author’s name, year of publication, geographic locations, the quality of the studies). (2) Baseline characteristics of the subjects in the eligible literature. (3) Detail of interventions or exposure factors. (4) The outcome indicators and outcome measures of interest (MD and SMD with the corresponding 95% CI).

Quality assessment

The quality of the selected studies was evaluated by two investigators using a revised tool for assessing risk of bias in Review Manager software. According to the Cochrane intervention research system evaluation manual 5.1.0, the document authenticity evaluation standard is carried out 16 . It mainly includes five aspects of bias (selection bias, performance bias, detection bias, attrition bias, reporting bias), six evaluation items: the generation method of random sequence, the concealment of random scheme allocation, the blind method of subjects and interventions, the blind method of outcome evaluators, the integrity of outcome data (loss of follow-up), and the possibility of selective reporting of research results. The single evaluation item is divided into three grades: (1) “low risk of bias” when a low risk of bias was determined for all domains, (2) “high risk of bias” when high risk of bias was reached for at least one domain or the study judgment included some concerns in multiple domains, and (3) unclear risk of bias 17 . The final quality evaluation grades of the literature are Grade A, grade B and grade C.

Statistical analysis

The main statistical software used in this study was ReviewManager5.3; Cochrane library) software. Measures such as length of hospital stay, duration of transitional oral feeding, and duration of parenteral nutrition use were statistically analyzed using the mean ± standard deviation and 95% CI. Standardized conversions could be performed with different measurement instruments to calculate MD/SMD values and 95% CI. The heterogeneity of included studies was examined by the I 2 index. If the test showed a high level of heterogeneity ( I 2  > 50%), a random effect model was used, otherwise a fixed-effect model ( I 2  < 50%) was used 18 . Sensitivity analysis was also performed to investigate the potential interference to the pooled effect size 19 . Statistical significance was set at P  < 0 0.05.

Ethics approval and consent to participate

This is a systematic review, no ethics review.

Literature search results

Initially, 322 literatures related to olfactory stimulation applied to premature infant were searched until July 28, 2022, of which 145 literatures related to human milk odor feeding of premature infants were screened. After excluding duplicate publications and those without full texts, 49 studies remained for full text screening. Reading through the full text, 26 articles were finally retained after excluding the inconsistent literature from the three aspects of study topic, overall design, and evaluation index. Then 12 articles were assessed for eligibility, and six randomized controlled studies were eventually included in the meta-analysis 20 , 21 , 22 , 23 , 24 , 25 (Fig.  1 ). General information and characteristics of the included literature are detailed (Table 1 ).

PRISMA flow diagram.

Quality assessment of the selected studies

The qualities of the six included literatures were evaluated as Grade B (partially meeting all criteria). Most of the studies failed to demonstrate the concealment of random scheme allocation and the blind method of outcome evaluators (Fig.  2 , Table 2 ).

Risk of bias assessment.

Effects of human milk odor stimulation on the transition time of oral feeding in premature infants

Five studies 20 , 21 , 22 , 23 , 24 evaluated the transition time of oral feeding for premature infants. Due to the small heterogeneity among studies ( P  = 0.39, I 2  = 3%), fixed-effect model analysis was conducted. The result showed that the transition time of oral feeding for premature infants in the intervention group was statistically significantly shorter than that in the routine care group,the statistical unit of the outcomes were days [SMD = − 0.48, 95% CI (− 0.69, − 0.27), Z = 4.54, P  < 0.00001] (Fig.  3 ).

Effects of human milk odor stimulation on the transition time of oral feeding for premature infants.

Effects of human milk odor stimulation on duration of parenteral nutrition in premature infants

Two studies 24 , 25 evaluated the duration of parenteral nutrition of premature infants. Due to the small heterogeneity among studies ( P  = 0.43, I 2  = 0%), the fixed effect model was used to analyze the duration of parenteral nutrition. The result indicated a statistically significantly shorter duration of parenteral nutrition support in the intervention group than that in the routine care group, the statistical unit of the outcomes were days [MD = − 1.01, 95% CI (− 1.70, − 0.32), Z = 2.88, P  = 0.004] (Fig.  4 ).

Effect of human milk odor stimulation on duration of parenteral nutrition for the premature infants.

Effects of human milk odor stimulation on the length of hospital stay for premature infants

Four 20 , 21 , 24 , 25 studies explored the impact of human milk odor stimulation intervention on the length of hospital stay. Random-effect model was applied given the high heterogeneity ( P  = 0.04, I 2  = 65%), which found no statistically significant difference between the intervention group and routine nursing care group [MD = − 0.28, 95% CI (− 1.19, 0.63), Z = 0.06, P  = 0.55] (Fig.  5 A). To explore the source of heterogeneity, sensitivity analysis was conducted by omitting one study at a time, which found that the study conducted by Yildiz could be have a significant impact the heterogeneity 20 . After removing this study 20 , heterogeneity was dramatically reduced ( P  = 0.50, I 2  = 0%), and fixed effect model was used to assess the effect of human milk odor stimulation intervention on the length of hospital stay. The result still showed no statistically significant difference between the two groups, the statistical unit of the outcomes were days [MD = − 0.03, 95% CI (− 0.41, 0.35), Z = 0.17, P  = 0.86] (Fig.  5 B).

( A ) Effects of human milk odor stimulation on the hospitalization time for premature infants before removing Yildiz’s study; ( B ) Effects of human milk odor stimulation on the hospitalization time for premature infants after removing Yildiz’s study.

The systematic review and meta-analysis of six randomized controlled studies found improved outcomes of premature infants associated with human milk odor stimulation. Premature infants are difficult to be fed through bottles by mouth due to underdeveloped oral motor function and uncoordinated sucking, swallowing and respiratory movements, which usually require formula or human milk delivered through a gastric tube 26 . In addition, oral exercise by oral intake contributes to weight gain and neurological development and accelerate their recovery process, while non-oral feeding deprives premature infants of oral exercise 27 , 28 , 29 . Moreover, prolonged tube feeding affects the oral motor skills of the child, leading to reduced respiratory coordination, late sensory problems, and malnutrition 30 . Malnutrition leads to lack of stable weight gain, prolonged hospitalization, and even neurological deficits and readmission 31 . In contrast, adequate nutrition, maintenance of weight gain, and physiological stability play crucial roles in the successful recovery of premature infants from hospitalization 22 . Therefore, the transition from parenteral or tube feeding to complete oral feeding will contribute significantly to sufficient nutrition and prompt recovery of premature infants. The results of the pooled analyses in the present study showed that human milk odor stimulation was able to reduce the time required for transition to normal oral feeding in premature infants. It is well known that normal oral feeding (sucking, swallowing and respiratory coordination) is an early sign of neuromotor integrity in premature infants and an important indication for hospital discharge 28 .

Premature infants admitted to the newborn intensive care unit (NICU) for further treatment and care after birth often require controlled number, frequency, and volume of feedings. Therefore, these newborns may lack adequate stimulation and sensory experiences related to feeding, such as hunger, fullness, taste, and smell 32 . Olfactory and gustatory stimulation alone or in combination can reduce gastrointestinal-related adverse reactions and effectively improve the nutritional status of premature infants by activating complex pathways and triggering cephalic responses, which increase intestinal motility, digestive enzyme secretion, and hormone release 33 , 34 , 35 . Therefore, the application of human milk odor stimulation plays a vital role in promoting the recovery process of premature infants.

Although human milk odor stimulation was associated with reduced transition time to oral feeding and short duration of parental feeding, it did not change the length of hospitalization. Hospitalization in premature infants is affected by a variety of confounding factors, such as body weight, gestational age, the occurrence of complications, family economic status, and medical environment factors. Relevant reports found that very low birth weight infants had significantly longer hospital stays. Moreover, it also revealed that the smaller the gestational age was, the more likely it would be to have complications such as infection, cerebral hemorrhage, and pulmonary hemorrhage, thus prolonging the length of hospital stay 36 . Due to differences in medical and economic levels in different countries, there will also be inconsistencies in the length of hospitalization for premature infants 37 .

It is noteworthy that the study by Küçük 23 , Beker 25 , Khodagholi 21 , and Yildiz 20 reported the weight of premature infants at discharge, and the mean weight at discharge of the control group of these infants in the four studies was 1933.10 ± 90.50 g, 2913 ± 577 g, 1588.1 ± 84.4 g, and 1922.25 ± 230.82 g, respectively. In contrast, the mean weight of the intervention group at discharge were 1908.00 ± 87.86 g, 2986 ± 672 g, 1565.6 ± 93.6 g, and 1893.50 ± 189.04, respectively. However, the difference of the discharge weight between the intervention group and the control group in each study was not statistically significant. Because the initial weight of the premature infants at admission was different between the control group and the intervention group, a simple comparison of body weight at discharge did not yield enough information. Therefore, the effects of the weight of premature infants were not included and observed.

Several inherent limitations need to be noticed when interpreting the results of this meta-analysis. First, the number of studies included was small with overall small sample size. Second, all included studies had a quality rating of B, which may have had an impact on the evaluation results. Third, other parameters, such as weight gain in premature infants, were not available and their effects were not assessed.

This systematic review and meta-analysis found that human milk odor could reduce the transition time to oral feeding and duration of parenteral nutrition for premature infants, suggesting a cheap, effective, and easily accessible method to improve the overall outcomes of premature infants. However, the findings are limited by the number and quality of included studies, therefore, more well-designed studies are still needed to verify our findings.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Yangyang Qin, Shu Liu, Yanming Yang & Yuan Zhong

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Yangyang Qin and Shu Liu were responsible for the topic selection, literature search, draft of the paper and data analysis. Han Han participated in literature screening, literature quality evaluation, and made the final revision of the paper. All authors conducted the search of literature, reviewed the articles, helped with data synthesis and interpretation, and played a major role in writing the manuscript. All authors agree to publish.

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Qin, Y., Liu, S., Yang, Y. et al. Effects of human milk odor stimulation on feeding in premature infants: a systematic review and meta-analysis. Sci Rep 14 , 8964 (2024). https://doi.org/10.1038/s41598-024-59175-4

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DOI : https://doi.org/10.1038/s41598-024-59175-4

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FNAIT Systematic Literature Review and Meta-Analysis Presented at the Academy of Managed Care Pharmacy 2024 Annual Meeting

  - Data Support HPA-1a Negative Frequency of More than 2% in Nearly 200,000 Screened Pregnant Women -

- Among HPA-1a Negative Pregnant Women, Approximately 33% are at Higher Risk for Alloimmunization -

Rallybio Corporation (Nasdaq: RLYB), a clinical-stage biotechnology company committed to identifying and accelerating the development of life-transforming therapies for patients with severe and rare diseases, today announced the presentation of results from a fetal and neonatal alloimmune thrombocytopenia (FNAIT) systematic literature review and meta-analysis at the Academy of Managed Care Pharmacy (AMCP) 2024 Annual Meeting, which is taking place in New Orleans, LA. The results of this research found that, in a pooled analysis of 198,062 pregnant women, 2.2% were HPA-1a negative and 32.3% of these women were also HLA-DRB3*01:01 positive and therefore at ~25-fold higher risk for alloimmunization. These rates are consistent with Rallybio’s current estimate of annual at-risk pregnancies and translate to tens of thousands of fetuses and newborns at risk each year for the potentially devastating consequences of FNAIT.

“We are pleased to establish a robust foundation of knowledge documenting the frequency of FNAIT risk as reported from a pooled analysis of peer-review literature, which is consistent with our current estimates,” said Stephen Uden, M.D., Chief Executive Officer of Rallybio. “This information, in combination with data from our ongoing FNAIT natural history study, will enable us to create a shared understanding of the number of pregnant women and babies at higher risk of FNAIT annually, underscoring the importance of having an effective preventative therapeutic option.”

Rallybio is developing RLYB212, a novel human monoclonal anti-HPA-1a antibody designed to prevent alloimmunization in pregnant women, thereby eliminating the risk of FNAIT and its potentially devastating consequences in their fetuses and newborns. Rallybio is on track to initiate a Phase 2 dose confirmation study for RLYB212 in pregnant women in the second half of 2024. The company is also conducting an ongoing FNAIT natural history study that will provide a contemporary dataset for HPA-1a alloimmunization frequency in a racially and ethnically diverse population, which is intended to support a future Phase 3 registration study for RLYB212. RLYB212 is the only investigational therapy currently reported to be in clinical development to address the needs of pregnant women at higher risk of FNAIT who have not alloimmunized.

The poster, titled “Fetal and Neonatal Alloimmune Thrombocytopenia: A Systematic Literature Review and Meta-analysis of Adverse Pregnancy-Related Outcomes to Support the Development of a Novel Prophylactic Therapeutic,” was presented by Andrea V. Margulis of RTI Health Solutions. Specifically, the literature review found that, of 198,062 screened pregnant women, 2.2% (95% confidence interval [CI], 2.0%-2.5%) were HPA-1a negative; 32.3% (28.6%-36.1%) of HPA-1a–negative women were HLA-DRB3*01:01 positive and therefore at even higher risk for alloimmunization. Approximately 10% of HPA-1a–negative women were already alloimmunized to HPA-1a. The meta-analysis is based on 12 observational cohort studies from Europe, Canada, and Egypt published from 1985 through 2018. A link to the poster is available here .

About FNAIT

Fetal and Neonatal Alloimmune Thrombocytopenia (FNAIT) is a potentially life-threatening rare disease that can cause uncontrolled bleeding in fetuses and newborns. FNAIT can arise during pregnancy due to an immune incompatibility between an expectant mother and her fetus in a specific platelet antigen called human platelet antigen 1, or HPA-1.

There are two predominant forms of HPA-1, known as HPA-1a and HPA-1b, which are expressed on the surface of platelets. Individuals who are homozygous for HPA-1b, meaning that they have two copies of the HPA-1b allele and no copies of the HPA-1a allele, are also known as HPA-1a negative. Upon exposure to the HPA-1a antigen, these individuals can develop antibodies to that antigen in a process known as alloimmunization. In HPA-1a-negative expectant mothers bearing a HPA-1a-positive fetus, alloimmunization can occur upon mixing of fetal blood with maternal blood. When alloimmunization occurs in an expectant mother, the anti-HPA-1a antibodies that develop in the mother can cross the placenta and destroy platelets in the fetus. The destruction of platelets in the fetus can result in severely low platelet counts, or thrombocytopenia, and potentially lead to devastating consequences including miscarriage, stillbirth, death of the newborn, or severe lifelong neurological disability in those babies who survive. There is currently no approved therapy for the prevention or prenatal treatment of FNAIT.

About Rallybio

Rallybio (Nasdaq: RLYB) is a clinical-stage biotechnology company with a mission to develop and commercialize life-transforming therapies for patients with severe and rare diseases. Rallybio has built a broad pipeline of promising product candidates aimed at addressing diseases with unmet medical need in areas of maternal fetal health, complement dysregulation, hematology, and metabolic disorders. The Company has two clinical stage programs: RLYB212, an anti-HPA-1a antibody for the prevention of fetal and neonatal alloimmune thrombocytopenia (FNAIT) and RLYB116, an inhibitor of complement component 5 (C5), with the potential to treat several diseases of complement dysregulation, as well as additional programs in preclinical development. Rallybio is headquartered in New Haven, Connecticut. For more information, please visit www.rallybio.com and follow us on LinkedIn and Twitter .

Forward-Looking Statements

This press release contains forward-looking statements that are based on our management’s beliefs and assumptions and on currently available information. All statements, other than statements of historical facts contained in this press release are forward-looking statements. In some cases, forward-looking statements can be identified by terms such as “may,” “will,” “should,” “expect,” “plan,” “anticipate,” “could,” “intend,” “target,” “project,” “contemplate,” “believe,” “estimate,” “predict,” “potential” or “continue” or the negative of these terms or other similar expressions, although not all forward-looking statements contain these words. Forward-looking statements in this press release include, but are not limited to, statements concerning the rates of women who are HPA-1a negative and HLA-DRB3*01:01 positive, the level of increased higher risk for alloimmunization among women who are HPA-1a negative and HLA-DRB3*01:01 positive, the actual rates of annual at-risk pregnancies for FNAIT and the number of potential pregnancies that could be at risk, our belief that the literature review establishes the relevant foundation of knowledge regarding such rates, our ability to create a shared understanding of the number of pregnant women and babies at higher risk of FNAIT annually, the timing of initiation of the Phase 2 dose confirmation study for RLYB212, whether the results of the natural history study and the planned Phase 2 dose confirmation study will be sufficient to support design and implementation of a Phase 3 registrational study for RLYB212, and the likelihood that Rallybio will be successful in developing RLYB212. The forward-looking statements in this press release are only predictions and are based largely on management’s current expectations and projections about future events and financial trends that management believes may affect Rallybio’s business, financial condition, and results of operations. These forward-looking statements speak only as of the date of this press release and are subject to a number of known and unknown risks, uncertainties and assumptions, including, but not limited to, our ability to successfully initiate and conduct our planned clinical studies, and complete such clinical studies and obtain results on our expected timelines, or at all, whether our cash resources will be sufficient to fund our operating expenses and capital expenditure requirements and whether we will be successful raising additional capital, our ability to enter into strategic partnerships or other arrangements, competition from other biotechnology and pharmaceutical companies, and those risks and uncertainties described in Rallybio’s filings with the U.S. Securities and Exchange Commission (SEC), including Rallybio’s Annual Report on Form 10-K for the period ended December 31, 2023, and subsequent filings with the SEC. The events and circumstances reflected in our forward-looking statements may not be achieved or occur and actual future results, levels of activity, performance and events and circumstances could differ materially from those projected in the forward-looking statements. Except as required by applicable law, we are not obligated to publicly update or revise any forward-looking statements contained in this press release, whether as a result of any new information, future events, changed circumstances or otherwise.

Investors Samantha Tracy Rallybio Corporation (475) 47-RALLY (Ext. 282) [email protected] Hannah Deresiewicz Stern Investor Relations, Inc. (212) 362-1200 [email protected] Media Victoria Reynolds Mission North (760) 579-2134 [email protected]

View source version on businesswire.com: https://www.businesswire.com/news/home/20240417889090/en/

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• Open access
• Published: 11 May 2023

Comparative efficacy and safety of PD-1/PD-L1 inhibitors in triple negative breast cancer: a systematic review and network meta-analysis of randomized controlled trials

• Ibrahim Elmakaty 1 ,
• Ruba Abdo 1 ,
• Ahmed Elsabagh 1 ,
• Abdelrahman Elsayed 1 &
• Mohammed Imad Malki   ORCID: orcid.org/0000-0002-6801-2126 2  

Cancer Cell International volume  23 , Article number:  90 ( 2023 ) Cite this article

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Triple-Negative Breast Cancer (TNBC) is a lethal subtype of breast cancer with limited treatment options. The purpose of this Network Meta-Analysis (NMA) is to compare the efficacy and safety of inhibitors of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) in treating TNBC.

Our search strategy was used in six databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature database, Embase, Scopus, and Web of Science up to November 2nd, 2022, as well as a thorough search in the most used trial registries. We included phase II and III randomized controlled trials that looked at the efficacy of PD-1/PD-L1 inhibitors in the treatment of TNBC and reported either Overall Survival (OS), Progression-Free Survival (PFS), or pathological Complete Response (pCR). The risk of bias was assessed utilizing Cochrane's risk of bias 2 tool, and the statistical analysis was performed using a frequentist contrast-based method for NMA by employing standard pairwise meta-analysis applying random effects model.

12 trials (5324 patients) were included in our NMA including seven phase III trials. Pembrolizumab in a neoadjuvant setting achieved a pooled OS of 0.82 (95% Confidence Interval (CI) 0.65 to 1.03), a PFS of 0.82 (95% CI 0.71 to 0.94) and a pCR 2.79 (95% CI 1.07 to 7.24) compared to Atezolizumab’s OS of 0.92 (95% CI 0.74 to 1.15), PFS of 0.82 (95% CI 0.69 to 0.97), and pCR of 1.94 (95% CI 0.86 to 4.37). Atezolizumab had less grade ≥ 3 adverse events (OR 1.48, 95% CI 0.90 to 2.42) than Pembrolizumab (OR 1.90, 95% CI 1.08 to 3.33) in the neoadjuvant setting.

Conclusions

PD-1/PD-L1 inhibitors exhibited varying efficacy in terms of OS, PFS, and pCR. They were associated with an increase in immune-related adverse effects. When used early in the course of TNBC, PD-1/PD-L1 inhibitors exert their maximum benefit. Durvalumab as a maintenance treatment instead of chemotherapy has shown promising outcomes. Future studies should focus on PD-L1 expression status and TNBC subtypes, since these factors may contribute to the design of individualized TNBC therapy regimens.

Systematic review registration PROSPERO Identifier: CRD42022380712.

Breast cancer remains a major health burden, causing considerable morbidity and mortality worldwide [ 1 ]. It has surpassed lung cancer as the most frequently diagnosed malignancy overall and ranks the fifth leading cause of cancer-related mortality, with an estimated 2.3 million new cases (11.7% of all cancers), and 685,000 deaths in 2020 [ 2 ]. The incidence rate has been increasing at an alarming rate over the past years, especially in transitioning countries, and it is predicted that by 2040, this burden will grow further by over 40% to about 3 million new cases and 1 million deaths every year [ 2 , 3 ]. Triple-Negative Breast Cancer (TNBC) is a particularly aggressive subtype that accounts for approximately 15–20% of all cases and is characterized by a lack of expression of both estrogen and progesterone receptors as well as human epidermal growth factor receptor 2 [ 4 ]. The high molecular heterogeneity, great metastatic potential, and limited therapeutic options have all contributed to TNBC having a relatively poor prognosis with a 5-year overall survival rate of 77% [ 5 , 6 ]. Due to the absence of well-defined molecular targets, TNBC therapy predominantly relies on the administration of Taxane and Anthracycline-based regimens in both the neoadjuvant and the adjuvant settings [ 4 , 6 , 7 ]. More favorable response rates are shown to be achieved when using a combination rather than single-agent chemotherapy [ 8 , 9 ]. Although this can be effective initially, chemotherapy is often accompanied by resistance, relapse, and high toxicity [ 10 , 11 ]. Additionally, survival rates in those who develop metastatic disease have not changed over the past 20 years [ 9 ]. The median Overall Survival (OS) for those patients with the current treatment option is 16 months and the median Progression-Free Survival (PFS) is 5.6 months [ 12 ]. These results underscore the urgent need for more effective and less toxic therapies.

The introduction of immunotherapy has revolutionized the field of oncology over the past decade and has been successfully incorporated into the standard treatment paradigm of many malignancies including non-small cell lung cancer and renal cell cancer [ 13 , 14 ]. Whilst breast cancer has traditionally been considered immunogenically quiescent, several lines of evidence have demonstrated TNBC to be highly immunogenic and feature a microenvironment that is enriched with stromal Tumor Infiltrating Lymphocytes (TILs) with a relatively high tumor mutational burden as opposed to other subtypes [ 15 , 16 ]. The high levels of inhibitory checkpoint molecules expressed on the TILs led to the successful implementation of Immune Checkpoint Inhibitors (ICI) in TNBC treatment, particularly inhibitors of the Programmed Cell Death 1 (PD-1) and the Programmed Cell Death Ligand 1 (PD-L1) which have shown great promise in the field’s clinical trials [ 15 ]. The PD‑L1/PD-1 signaling pathway exerts a critical role in forming an adaptive immune resistance mechanism that mediates tumor invasion and metastasis [ 17 ]. Blocking this pathway would therefore restore the antitumor immune responses by reducing the inhibition of innate immunity and reactivating tumor-specific cytotoxic T cells [ 18 ].

Atezolizumab, an anti-PD-L1 antibody was the first Food and Drug Administration (FDA) approved ICI given along with nab-paclitaxel for patients with unresectable locally advanced or metastatic TNBC whose tumors express PD-L1 [ 19 ]. This accelerated approval was based on the results of the Impassion130 trial. Unfortunately, the designated confirmatory trial, IMpassion131 neither met the primary endpoint of PFS superiority nor achieved statistically significant overall OS leading to the withdrawal of this combination as an indication for treatment [ 12 ]. Alternatively, FDA granted approval to pembrolizumab, a PD-1 inhibitor to be used in combination with chemotherapy for patients with high-risk, early-stage TNBC, as well as those with locally recurrent unresectable or metastatic TNBC whose tumors have a PD-L1 Combined Positive Score (CPS) of ≥ 10 [ 12 ]. Nonetheless, there remain several additional clinical trials that have assessed the role of anti‑PD‑L1/PD‑1 agents in TNBC treatment with inconsistent results. The objective of this Network Meta-Analysis (NMA) is to evaluate the efficacy and safety of these agents, as well as compare them in order to determine the optimal therapeutic regimen for patients with TNBC.

Protocol and registration

This systematic review and meta-analysis is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for NMA Additional file 1 : (Table S1) [ 20 ]. The NMA protocol was carried in accordance with a protocol that had been registered in the International Prospective Register of Systematic Reviews (PROSPERO) online database (PROSPERO Identifier: CRD42022380712).

Search strategy

We developed our search strategy in the PubMed database using Medical Subject Headings (MeSH) that included the terms (“Immune Checkpoint Inhibitors”[MeSH] OR “programmed cell death 1 receptor/antagonists and inhibitors”[MeSH]) AND “Triple Negative Breast Neoplasms”[MeSH] AND “Randomized Controlled Trial”[Publication Type] with multiple keywords build around them. There was no date or language restriction applied to our strategy. The developed search strategy was then transferred from PubMed to five other databases by the Polyglot translator [ 21 ], namely Cochrane Library, Cumulative Index to Nursing and Allied Health Literature database, Embase, Scopus, and Web of Science. All databases were searched from the inception date until the 2nd of November 2022. The yielded studies were then exported to EndNote X7, where duplicates were identified and excluded. The remaining articles were uploaded to the Rayyan platform for screening [ 22 ]. In addition, we searched popular clinical trial registries such as ClinicalTrials.gov, EU Clinical Trials Register, International Standard Randomised Controlled Trial Number registry, International Clinical Trials Registry Platform, and breastcancertrials.org for Gery literature (unpublished trials) to ensure the comprehensiveness of our search strategy. Additional file 1 contains the complete strategy for each database and trial registries.

Eligibility criteria

We included trials that met the following criteria: (1) usage of FDA-approved PD-1/PD-L1 inhibitors, (2) phase II or III RCTs, (3) for the management of confirmed TNBC, (4) compared against a different Immune Checkpoint Inhibitors (ICIs), multiple agents’ chemotherapy regimen, single agent chemotherapy regimen or placebo (5) reported Hazard Ratios (HR) for OS, PFS or numbers of pathological Complete Response (pCR) in each both arms of the trial. We excluded review articles, non-randomized trials, quasi-randomized trials, meta-analyses and observational studies, as well as studies on animal models. We also excluded trials using non-FDA-approved immune checkpoint inhibitors.

Study selection and screening

The records obtained from applying the search strategy were evaluated on the Rayyan platform [ 22 ]. Titles and abstracts were screened independently by two reviewers either IE/RA or AhE/AbE with any disagreements were resolved by consensus among the entire team (IE, RA, AhE, AbE and MIM). The full texts of studies that were deemed potentially eligible were then retrieved and double-screened independently (IE/RA or AhE/AbE), with discrepancies dealt with through discussion with the whole team (IE, RA, AhE, AbE and MIM).

Data extraction

We extracted information from each eligible study on the first author, publication date, phase, total number of patients included, and number of patients in each arm, as well as patient demographics (median age, cancer stage), treatment given in each arm, duration of treatment, follow-up time and percentage of patients with positive PD-L1 expression at baseline defined by CPS ≥ 1. We also extracted HR values and their 95% Confidence Intervals (CI) for OS and PFS from each study, as well as the number of patients who achieved pCR in both arms. We collected data on the occurrence of common Adverse Events (AEs) in patients from each study arm. When duplicate publications were discovered, only the most recent and complete reports of RCTs were included. Two reviewers extracted all data (IE/RA or AhE/AbE), which was then summarized, discussed by the team, and compiled into an online Microsoft Excel spreadsheet accessible to all authors.

Risk of bias assessment

To assess the risk of bias, version 2 of the Cochrane Risk-Of-Bias (RoB2) assessment tool for randomized trials was used [ 23 ]. This was done independently by the reviewers (IE/RA or AhE/AbE) with disagreement being resolved by discussion and input from a third author (MIM). The RoB2 assessment tool includes five distinct domains with multiple signaling questions to aid in assessing the risk of bias. The five domains in this tool appraise bias arising from the following: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome and selection of the reported result. Accordingly, the signaling questions provided by the ROB2 tool were answered, and the two other reviewers evaluating the trial used those answers to categorize the current domain as “low risk of bias,” “some concerns,” or “high risk of bias.” The reviewer's judgment in each domain resulted in an overall risk-of-bias conclusion for the trial under consideration. The study was deemed to have a “low risk of bias” if it was judged to have a low risk of bias in all domains included in the tool, “some concerns” if it raised some concerns in at least one domain, or “high risk of bias” if it was judged to have a high risk of bias in at least one or some concerns for multiple domains, significantly lowering confidence in the result. This data for all studies was compiled in the tool's template excel sheet, which was made available to all reviewers.

As our aim is to evaluate the efficacy and safety of ICIs, we selected four different outcomes in this NMA. The first two are OS, which is defined as the time from randomization to death from any cause, and PFS, which is defined as the time from randomization to the first documented disease progression per Response Evaluation Criteria in Solid Tumors version 1.1. The HR and its 95% CI comparing the two arms of the trials in Intention-To-Treat (ITT) populations were used to generate our final effect sizes in this NMA. The third outcome is pCR, which is defined as the absence of invasive tumors in the breast and regional nodes at the time of definitive surgery (ypT0/is pN0). Finally, to assess the safety of PD-1/PD-L1 inhibitors, we estimated the likelihood of developing AEs in each arm of the ITT populations by using the number of patients who had AEs in all grades and grade 3 or higher. Both pCR and AEs were calculated using Odds Ratios (OR) and their 95% CI based on the number of reported events in each of the trial arms.

Data analysis

Our NMA used standard pairwise meta-analysis implemented in multivariate meta-analysis models using a frequentist contrast-based approach [ 24 ]. If there is no evidence of importance in transitivity, a random-effects frequentist NMA has to be performed. These models assume that direct and indirect evidence are consistent. The network meta-analysis' net evidence is a weighted average of direct and indirect evidence. For OS and PFS, we calculated the mean log HR and its standard error and entered it into the model [ 25 ], while for pCR and AEs, we entered the number of events in each arm. When the same intervention was used in both arms of an RCT, it was assumed that the effect of that intervention was cancelled out, thus we assumed that all trials used the same comparator chemotherapy, which is necessary because even within the same trial, different chemotherapy regimens were used as controls. The assumption of transitivity was tested by comparing the distribution of study and population characteristics that may act as effect modifiers across the various pairwise comparisons. If transitivity issues were present, we returned to data extraction to verify the stage of TNBC, and the type of chemotherapy regimen used. In the case of indirect evidence, inconsistency between direct and indirect evidence was investigated locally through the use of symmetrical node-splitting [ 26 ]. However, we found no head-to-head comparisons of PD-1/PD-L1 inhibitors. Visual inspection of comparison-adjusted funnel plots for NMA was used to assess publication bias [ 27 ]. Studies were expected to form an inverted funnel centred at zero in the absence of small-study effects. The Surface Under the Cumulative Ranking Curve (SUCRA) value, which represents the re-scaled mean ranking, was also calculated and summarized [ 28 ]. Where quantitative synthesis is deemed invalid due to a small number of studies using the same intervention, narrative synthesis was used to report the findings in the results section, with estimates from the original studies. For all comparisons, we adopted the network suite in Stata to perform analyses and graphs, Stata version 16 (College Station, TX, USA) [ 29 ].

Subgroup analysis

In the event of significant heterogeneity, we conducted a sensitivity analysis, removing each study and comparing its effect. In terms of the outcome of AEs, we investigated the impact of reported symptoms on AEs to check which side effects are likely to produce this effect. We performed a sensitivity analysis for NMA using the Generalized Pairwise Modelling (GPM) framework to investigate the effect of the models used [ 30 ]. The GPM framework was used to generate mixed treatment effects against a common comparator. The common comparator for all outcomes was chemotherapy. Other than transitivity, this framework requires no additional assumptions [ 30 ]. In this sensitivity analysis, the Inverse Variance Heterogeneity model was used to pool the meta-analytical estimates [ 31 ]. The H index was used to assess statistical heterogeneity across pooled direct effects, while the weighted pooled H index ( \(\overline{H }\) ) was used to examine inconsistency across the network and assess transitivity [ 30 ]. The smallest value that H and \(\overline{H }\) can take is 1, and \(\overline{H }\) <3 was thought to represent minimal inconsistency [ 32 ]. MetaXL version 5.3 was used for the GPM framework analyses (EpiGear Int Pty Ltd.; Brisbane, Australia). The results of those sensitivity analyses will be presented in the Additional file 1 .

Study selection

Figure  1 illustrates the PRISMA flow diagram of the study selection process. Our extensive database and trial registry search yielded 1583 results. 397 duplicates were automatically removed through EndNote. A total of 1186 potentially relevant articles were identified, of which 1056 were excluded after the initial review of their titles and abstracts. The full text of the remaining 130 articles was assessed for eligibility. Of those, 71 were found to be duplicate patient records, and only the most recent and inclusive records were kept. Another 31 RCTs were excluded due to a paucity of outcome measures at the time of the search. Other 16 records were similarly removed for a variety of reasons depicted in Fig.  1 . Eventually, 12 studies were eligible for inclusion in our NMA [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Additional file 1 : Table S2 includes all the additional information on the omitted record citations as well as full reasoning.

PRISMA flowchart showing the number of studies at each stage of conducting this NMA

Study characteristics and data collection

Table 1 summarizes the characteristics of the included RCTs. All 12 trials included were two-arm trials that reported results from 5324 patients with median ages ranging from 48 to 59.1 years. There were seven phase III trials and five phase II trials. Six studies looked at the effect of PD-1/PD-L1 inhibitors on unresectable, invasive, or metastatic (advanced) TNBC [ 33 , 35 , 36 , 37 , 40 , 43 ], four looked at non-metastatic/early-stage TNBC [ 39 , 41 , 42 , 44 ], and two looked at treated metastatic TNBC for maintenance therapy [ 34 , 38 ]. Atezolizumab (n = 5 trials) was the most commonly studied ICI [ 33 , 36 , 39 , 40 ], followed by Pembrolizumab (n = 4 trials) [ 34 , 35 , 41 , 42 ], Durvalumab (n = 2 trials) [ 37 , 38 ], and Nivolumab (n = 1 trial) [ 43 ]. Six trials used multiple-agent chemotherapy regimens in combination with PD-1/PD-L1 inhibitors [ 36 , 37 , 39 , 41 , 42 , 44 ], and four used mono-chemotherapy regimens with PD-1/PD-L1 inhibitors, including two Taxane-based [ 33 , 40 ], one Platinum-based [ 43 ], and one Investigator's choice chemotherapy [ 35 ]. The other two trials compared PD-1/PD-L1 inhibitors alone to chemotherapy for maintenance therapy in patients with previously treated metastatic TNBC [ 34 , 38 ]. There were some minor differences in the duration of PD-1/PD-L1 inhibitors used between studies. With the exception of one trial [ 44 ], PD-1/PD-L1 inhibitors were used for four to eight cycles with a follow-up time of more than 12 months. The PD-L1 expression in TNBC tissue samples varied significantly between the included RCTs, ranging from 39 to 87% (see Table 1 ). Table 1 is to be inserted here.

Overall, five RCTs had a low risk of bias [ 33 , 35 , 37 , 40 , 41 ], six had some concerns [ 36 , 38 , 39 , 42 , 43 , 44 ], and only one had a high risk of bias [ 34 ]. When following the intended protocol and performing ITT analysis, all included trials were of high quality. Five of the six trials that raised concerns were due to the trial being non-blinded [ 36 , 38 , 42 , 43 , 44 ], which could affect the assessment of the outcome of interest. One study found a significant difference in one of the baseline parameters [ 39 ], while the high-risk study failed to report one of the secondary outcomes in the main text [ 34 ]. Figure  2 depicts the overall risk of bias across all domains (Fig.  2 A), as well as the reviewers' judgment within each domain for all included trials (Fig.  2 B).

The results of the risk of bias assessment. A Stacked bar chart showing a summary of the risk of bias assessment overall and in each domain. B The detailed answers for all studies in each domain

• Overall survival

The OS was reported in nine RCTs [ 33 , 34 , 35 , 37 , 38 , 39 , 40 , 41 , 43 ], three of which used Atezolizumab [ 33 , 39 , 40 ], two used Pembrolizumab [ 35 , 41 ], and one used either Durvalumab or Nivolumab as a neoadjuvant to chemotherapy (Fig.  3 A) [ 37 , 43 ]. Pembrolizumab in a neoadjuvant setting had a pooled HR of 0.82 (95% CI 0.65 to 1.03, SUCRA = 46%, n = 2 trials, 1449 patients), which was comparable to Atezolizumab’s HR of 0.92 (95% CI 0.74 to 1.15, SUCRA = 28%, n = 3 studies, 1886 patients), demonstrating a prolonged but insignificant OS in PD-1/PD-L1 inhibitors arms (see SUCRA Additional file 1 : Table S3). GeparNuevo using Durvalumab had the only significant reported prolonged OS in PD-1/PD-L1 inhibitors in neoadjuvant settings (HR 0.24, 95% CI 0.08 to 0.72) [ 37 ]. Durvalumab also improved OS when used as a monotherapy for maintenance therapy in patients with metastatic TNBC (SAFIR02-BREAST trial, HR 0.54, 95% CI 0.30 to 0.97) [ 38 ]. This outcome's results were consistent among the studies. The rest of the analysis is shown in Fig.  3 . GPM sensitivity analysis also revealed no significant differences (Additional file 1 : Figure S1).

Overall survival network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the effectiveness of each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately

• Progression-free survival

Only six RCTs reported PFS [ 33 , 34 , 35 , 38 , 40 , 43 ], two of which used Atezolizumab in neoadjuvant sitting [ 33 , 40 ], as shown in Fig.  4 A. In a neoadjuvant setting along with chemotherapy, Atezolizumab achieved a pooled PFS HR of 0.82 (95% CI 0.69 to 0.97, SUCRA = 76.5%, 1553 patients) (see complete SUCRA values in Additional file 1 : Table S4), whereas Pembrolizumab can also prolong PFS as reported in KEYNOTE-355 trial when combined with chemotherapy (HR 0.82, 95% CI 0.71 to 0.94) [ 35 ]. In the SAFIR02-BREAST trial, Durvalumab had similar PFS to single-agent chemotherapy (HR 0.87, 95% CI 0.54 to 1.42, 82 patients) [ 38 ], whereas Pembrolizumab alone was associated with significantly worse PFS than chemotherapy in KEYNOTE-119 trial (HR 1.60, 95% CI 1.33 to 19.2, 622 patients) [ 34 ]. The rest of the analysis is shown in Fig.  4 , and the GPM sensitivity analysis is illustrated in the Additional file 1 : (Figure S2).

Progression-free survival network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the effectiveness of each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately

Pathologic complete response

The number of patients who achieved a complete response was reported in six trials [ 36 , 39 , 41 , 42 , 44 ]: three on Atezolizumab [ 36 , 39 , 44 ], two on Pembrolizumab [ 41 , 42 ], and one on Durvalumab [ 37 ], all in the neoadjuvant setting to chemotherapy. Pembrolizumab in combination with chemotherapy significantly increased the odds of achieving pCR compared to chemotherapy alone (OR 2.79, 95% CI 1.07 to 7.24, SUCRA = 82.1%, 2 studies, 709 patients), whereas Atezolizumab showed an insignificant increase in pCR (OR 1.94, 95% CI 0.86 to 4.37, SUCRA = 62.3, 3 studies, 674 patients) (complete SUCRA values in Additional file 1 : Table S5). In the GeparNuevo trial, the calculated OR of achieving pCR with Durvalumab and chemotherapy was 1.45 (95% CI 0.80 to 2.63) [ 37 ]. Figure  5 summarizes the results of the pCR analysis, and the GPM sensitivity analysis is presented in the Additional file 1 : Figure S3.

Pathologic complete response network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the effectiveness of each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately

• Adverse events

At the time of analysis, nine trials had AEs grade ≥ 3 results reported [ 33 , 34 , 35 , 36 , 39 , 40 , 41 , 42 , 44 ], the majority of which was the effect of Atezolizumab combined with chemotherapy versus chemotherapy alone (n = 5 studies) [ 33 , 36 , 39 , 40 , 44 ], followed by Pembrolizumab with chemotherapy (n = 3 studies) (Fig.  6 A) [ 35 , 41 , 42 ]. The pooled OR of Atezolizumab addition to chemotherapy causing AEs grade 3 or more compared to chemotherapy alone was 1.48 (95% CI 0.90 to 2.42, 5 studies, 2325 patients), whereas Pembrolizumab with chemotherapy showed a slightly greater risk of causing AEs grade ≥ 3 (OR 1.90, 95% CI 1.08 to 3.33, 3 studies, 2263 patients) (Fig.  6 C). Atezolizumab and Pembrolizumab achieved SUCRA values of 26.7% and 9.3% respectively compared to 64.3% for chemotherapy (Additional file 1 : Table S6). When compared to single-agent chemotherapy, the KEYNOTE-119 trial showed a significant reduction in AEs grade ≥ 3 when using Pembrolizumab alone in maintenance therapy (OR 0.29, 95% CI 0.19 to 0.43) [ 34 ].

Grade ≥ 3 adverse events network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing adverse events for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately

In the sensitivity analysis investigating the subtype of the reported AEs, neoadjuvant Pembrolizumab to chemotherapy showed an increase in the odds of developing adrenal insufficiency (OR 26.24, 95% CI 3.50 to 197.86, Additional file 1 : Figure S4), diarrhea (OR 1.47, 95% CI 1.14 to 1.88, Additional file 1 : Figure S5), hyperthyroidism (OR 5.22, 95% CI 2.44 to 11.15, Additional file 1 : Figure S6), hypothyroidism (OR 5.23, 95% CI 3.35 to 8.16, Additional file 1 : Figure S7), infusion reaction (OR 1.64, 95% CI 1.13 to 2.37, Additional file 1 : Figure S8) and pneumonitis (OR 5.94, 95% CI 1.29 to 27.27, Additional file 1 : Figure S9). On the other hand, Atezolizumab in the neoadjuvant settings increased the odds of developing hyperthyroidism (OR 10.91, 95% CI 1.98 to 60.15, Additional file 1 : Figure S6), hypothyroidism (OR 3.77, 95% CI 2.52 to 5.63, Additional file 1 : Figure S7) and pneumonitis (OR 2.73, 95% CI 1.41 to 5.31, Additional file 1 : Figure S9) compared to chemotherapy alone. The remaining results of the sensitivity analysis according to the type of AE developed and GPM are outlined in the Additional file 1 : (Figure S10 to Figure S17).

Principle findings and existing literature

TNBC is an aggressive form of breast cancer that is often associated with poor patient outcomes, largely due to the limited treatment options available [ 6 ]. Intensive research efforts have therefore attempted to improve the efficiency of standard-of-care chemotherapy by incorporating immunotherapeutic agents, particularly ICIs, which have emerged as a novel breakthrough in cancer treatment in the past recent years [ 15 ]. The present network meta-analysis aimed to compare the published data on the efficacy and safety of ICIs in treating TNBC. Our results showed that antiPD-1/PD-L1 therapies can be used as a neoadjuvant to chemotherapy in the first-line treatment or alone in previously treated TNBC. Multiple RCTs that were conducted on this topic have demonstrated a greater benefit of adding ICIs to chemotherapy in terms of OS, PFS, and pCR [ 45 , 46 , 47 , 48 , 49 ]. As a result, existing meta-analyses evaluating those trials were successful in achieving statistical and clinical significance. For example, Zhang et al. group reported that PD-1/PD-L1 inhibitors in combination with chemotherapy improved pCR (OR 1.59, 95% CI 1.28 to 1.98), event-free survival (HR 0.66, 95% CI 0.48 to 0.91, p = 0.01), and overall survival (HR 0.72, 95% CI 0.52 to 0.99) in TNBC patients compared to chemotherapy alone [ 45 ]. Moreover, Li et al. studied the pCR of ICIs in neoadjuvant setting in TNBC and reported that the OR significantly increased in their four included study meta-analysis (OR 2.14, 95% CI 1.37–3.35, P < 0.001) and a better event-free survival (HR 0.66, 95% CI 0.48 to 0.89, P = 0.007) [ 49 ], while similar values for pCR were reported by Rizzo et al. (OR 1.95, 95% CI 1.27 to 2.99) and Xin et al. (OR 1.91, 95% CI 1.32 to 2.78) [ 46 , 48 ]. Villacampa et al. reported that patients with PD-L1-positive tumors had a significantly better PFS with ICIs (HR 0.67, 95% CI 0.58 to 0.79) and a trend towards better OS (HR 0.79, 95% CI 0.60 to 1.03), while no benefit was observed in patients with PD-L1-negative tumors [ 47 ]. This is in contrast to Zhang et al. who found that the pCR rate was almost identical in the PD-L1-positive and negative groups [ 45 ]. However, many have reported high heterogeneity in effect estimates, indicating major systematic differences between the included RCTs [ 45 , 46 , 47 , 48 , 49 ]. Although this heterogeneity has been attributed to many factors including patient population, TNBC stage, PD-L1 levels, randomization process, and type of chemotherapy regimen, these meta-analyses have failed to acknowledge the performance differences and the distinct immunologic mechanisms by which ICIs act. Contrary to our study, they have combined all agents into a large group and regarded them as one entity, assuming they have similar efficacy and safety.

Efficacy of PD-1/PD-L1 inhibitors

In our NMA, only two trials out of nine reported statistical significance in terms of OS, both of which used Durvalumab, one as a neoadjuvant (GeparNuevo phase II trial, HR 0.24, 95% CI 0.08 to 0.72, 174 patients) and the other as maintenance (SAFIR02-BREAST trial, HR 0.54, 95% CI 0.30 to 0.97) [ 37 , 38 ]. Six of the remaining seven trials reported longer, yet statistically insignificant survival. This could be attributed to the small sample size or the lack of follow-up, yet the possibility of Durvalumab having superior efficacy remains, highlighting the need for an additional large phase III RCTs investigating Durvalumab efficacy and safety in TNBC. Five of the seven trials that used neoadjuvant PD-1/PD-L1 inhibitors and reported OS were invasive or metastatic (advanced), with only GeparNuevo achieving a significant reduction in OS HR (0.24, 95% CI 0.08 to 0.72). The remaining two neoadjuvant trials (IMpassion031 trial, HR 0.69, 95% CI 0.25 to 1.87) and (KEYNOTE-522 trial, HR 0.72, 95% CI 0.51 to 1.02) were on non-metastatic or advanced and did not show any improvement in OS.

In general, PFS prolongation followed a positive trend similar to OS when ICIs were used. The IMpassion130 trial demonstrated a significant improvement in PFS with Atezolizumab (HR 0.80, 95% CI 0.69 to 0.92) [ 40 ], as opposed to the confirmatory trial Impassion131which failed to achieve statistical significance with Atezolizumab despite extending PFS (HR 0.86, 95% CI 0.70 to 1.05) [ 33 ]. An FDA review of the discordant findings between these two trials, including chemotherapy regimens, study design, conduct and population found no single component that could be responsible for this discrepancy, as a result, the reason for this is unclear at present. It is also worth mentioning that the only two trials that reported statistical significance, KEYNOTE-355 and IMpassion130, are the ones with the largest population sample, which may have accounted for their outcome.

Alternatively, the KEYNOTE-355 trial found that Pembrolizumab is effective in prolonging PFS in the neoadjuvant setting (HR 0.82, 95% CI 0.69 to 0.97) [ 35 ], while Nivolumab appears to be less effective in improving survival PFS (HR 0.98, 95% CI 0.51 to 1.88). Both KEYNOTE-522 and IMpassion031 trials found that using ICI in the neoadjuvant setting improved disease-free survival [ 39 , 41 ]. ICIs use as maintenance therapy instead of chemotherapy in treated metastatic TNBC has also shown promising results in terms of prolonging survival using Durvalumab in the SAFIR02-BREAST trial, in contrast to Pembrolizumab that showed no significant improvement in the Keynote-119 trial (PFS HR 1.6, 95% CI 1.33 to 1.92) [ 34 , 38 ]. Nonetheless, the Keynote-119 trial demonstrated a significant reduction in AEs grade ≥ 3, negating one of chemotherapy's worst attributes [ 34 ]. Furthermore, ICIs have also been shown to improve the chances of achieving pCR in TNBC patients when compared to chemotherapy alone. According to our NMA, neoadjuvant Pembrolizumab resulted in the highest pCR (OR 2.79, 95% CI 1.07 to 7.24), followed by Atezolizumab (OR 1.94, 95% CI 0.86 to 4.37, 3 studies, 674 patients), and Durvalumab, which had the lowest pCR (1.45, 95% CI 0.80 to 2.63). However, among the six trials that reported pCR, NeoTRIPaPDL1 and GeparNuevo were the only two RCTs that did not report significant improvement in pCR [ 36 , 37 ]. This can be explained by the advanced TNBC stage both studies were conducted upon, implying that using ICIs at an earlier stage of TNBC disease progression will more likely benefit patients and improve their survival. This is supported by the fact that early-stage TNBC has a greater tumor immune microenvironment than advanced TNBC, which increases the effectiveness of ICIs with the additional stimulation to the immune response provided by chemotherapy treatment [ 46 ]. Another possibility for the negative NeoTRIPaPDL1 results could be due to the insufficient immune induction effect of the chemotherapy regimens used in the study design [ 46 ].

Safety of PD-1/PD-L1 inhibitors

In regard to safety, ICIs appear to be associated with a significant toxicity burden, especially in the form of immune-related AEs [ 50 ]. Our NMA showed that Pembrolizumab generally has a worse safety profile than Atezolizumab, causing more grade ≥ 3 AEs (OR 1.90, 95% CI 1.08 to 3.33). Despite the fact that both drugs increased the risk of hyperthyroidism, hypothyroidism, and pneumonitis, Pembrolizumab caused a significant increase in adrenal insufficiency, diarrhea, and infusion reaction, making Atezolizumab a safer option. These AEs are likely to be related to drugs’ mechanism of action. The ability of ICIs to reinvigorate exhausted T-cells in an attempt to kill the tumor may destroy the immune tolerance balance and result in autoimmune and inflammatory responses in normal tissue [ 51 , 52 ]. However, the reason why certain people or specific organs are more susceptible than others is still incompletely understood [ 51 ]. Proposed hypotheses include hereditary predisposition, environmental factors and expression of shared antigens between tumors and affected tissue [ 51 ]. Whilst most of these immune-related AEs are usually manageable and reversible, some may require long-term intervention, such as endocrinopathies [ 50 ]. Of note, close monitoring of patients and early detection of any AEs is of utmost importance to ensure patients can benefit from adding PD-1/PD-L1 inhibitors to their chemotherapy regimen. Careful follow-up care is also warranted to prevent potential later onset immune-related AEs that may present after cessation of ICIs [ 50 ].

Enhancing the benefit of PD-1/PD-L1 inhibitors

It is crucial to note that the response to ICIs as well as to the combination of other agents differs significantly among patients, highlighting the importance of predictive biomarkers [ 53 ]. A multitude of promising novel biomarkers has recently gained considerable attention including the CD274 gene and TILs, but to date, PD-L1 status remains the only biomarker approved to guide patient selection in TNBC [ 53 , 54 , 55 ]. We considered PD-L1 positivity as CPS ≥ 1 in Table 1 , yet the threshold for PD-L1 positivity and at what level ICIs become more effective remains a topic of scientific debate. Analysis of the present NMA showed that IMpassion031, Keynote-522, and GeparNuevo trials have all demonstrated PD-1/PD-L1 inhibitors to improve efficacy regardless of PD-L1 status in patients with early-stage TNBC [ 33 , 41 ]. Conversely, IMpassion130 and Keynote-355 demonstrated improved efficacy in metastatic TNBC but not in early-stage TNBC [ 35 , 40 ]. Following the outcomes of the recently published IMpassion130 and KEYNOTE-355 trials, this biomarker was validated as a predictor of response to PD-1/PD-L1 inhibitors in metastatic breast cancer [ 48 ]. Even though data from a previous meta-analysis found no correlation between pCR rates and PD-L1 expression, further investigation revealed pCR rates to be higher in PD-L1-positive patients [ 46 ]. Notably, the lack of a standardized approach for PD-L1 detection in TNBC has led to inconsistent PD-L1 prevalence, thereby hampering the precise guiding of immunotherapy [ 45 , 54 ]. Another significant challenge is that TNBC is composed of numerous heterogeneous subtypes. Biomarker research on IMpassion130 samples revealed that PD-L1 is expressed higher in basal-like immune-activated subtype (75%) and immune-inflamed tumors (63%) TNBC subtypes [ 56 , 57 ]. Another exploratory study found an improved advantage in PFS in TNBC patients with immune-inflamed tumors, basal-like immune-activated and basal-like immunosuppressed subtypes, in addition to the prolonged OS in inflamed tumors and basal-like immune-activated subtypes [ 47 , 56 , 57 ]. Certainly, the identification of predictive biomarkers of efficacy will greatly aid in optimizing personalized regimens for TNBC patients, as well as predicting the long-term effectiveness of PD-1/PD-L1 inhibitors.

Future RCTs using PD-1/PD-L1 inhibitors in TNBC

Interestingly, the majority of the currently ongoing RCTs are investigating Atezolizumab and Pembrolizumab, both of which were studied the most in nine out of the 12 RCTs included in our NMA. Hoffmann-La Roche, the sponsor of IMpassion130, IMpassion131, and Impassion031, is currently funding three additional phase III RCTs on Atezolizumab. IMpassion132 is a double-blind Phase III RCT on the efficacy and safety of neoadjuvant Atezolizumab for early relapsing TNBC (NCT03371017), while IMpassion030 is planned to be the largest RCT on ICI as it is presently recruiting 2300 patients with operable TNBC to investigate the combination of neoadjuvant Atezolizumab and chemotherapy (NCT03498716). Hoffmann-La Roche’s third RCT is looking into the combination of Atezolizumab, Ipatasertib, and Paclitaxel in patients with advanced or metastatic TNBC (NCT04177108). In another phase III double-blinded RCT, GeparDouze will investigate neoadjuvant Atezolizumab followed by adjuvant Atezolizumab in patients with high-risk TNBC (NCT03281954). The National Cancer Institute (NCI) is also funding a large phase III RCT to assess the efficacy and safety of Pembrolizumab as adjuvant therapy following neoadjuvant chemotherapy (NCT02954874). Additionally, ASCENT-04 and ASCENT-05 are both ongoing phase III RCTs investigating the PFS of Pembrolizumab in combination with Sacituzumab Govitecan versus chemotherapy in either advanced or residual invasive TNBC (NCT05382286, NCT05633654). TROPION-Breast03 is similarly a new phase III RCT looking at Datopotamab Deruxtecan (DatoDXd) with or without Durvalumab in early-stage TNBC (NCT05629585). Finally, Avelumab, another PD-L1 inhibitor, is currently being studied in a phase III RCT on high-risk TNBC patients (A-Brave trial, NCT02926196).

Limitations

There are some limitations that must be addressed in this NMA. Firstly, only 12 studies were included, in addition to the limited number of reported outcomes of interest. This is primarily due to the fact that we only included phase II and phase III RCTs because our goal was to compare the efficacy of PD-1/PD-L1 inhibitors in clinical settings. With the ongoing development of neoadjuvant ICI clinical trials, there will certainly be more comprehensive data to be analyzed in future NMA. Second, the NMA comparisons were solely based on direct evidence, with no head-to-head comparisons of neoadjuvant ICIs in TNBC. Moreover, the small number of studies has caused the limited network connectivity to produce large confidence intervals for some estimates, even when effect sizes were large. It may have also resulted in an immature investigation of heterogeneity and publication bias. We would also like to point out the differences between the included studies in terms of TNBC stage, chemotherapy backbone, ICI duration, follow-up time, and PD-L1 expression status. Different chemotherapy backbone regimens used in different studies may have influenced the interpretation of the results as they could have been added to separate groups in the NMA if the number of included studies allowed. Given this heterogeneity and the limited RCTs number, further subgroup analysis based on PD-L1 expression status and nodal involvement, as well as advanced vs early-stage, was not deemed feasible. Finally, all data in this study were derived from published literature, and no individual patient data were used. Noteworthily, the meta-analysis results could potentially be biased by two of the included RCTs that were published as abstracts, which may have relatively incomplete data, missing safety data, and unclear research methods.

Our NMA found variation in efficacy and safety among PD-1/PD-L1 inhibitors used to treat TNBC, as well as significant systematic differences between the RCTs included. To better assess those variations in efficacy, head-to-head trials between those PD-1/PD-L1 inhibitors are needed. In their use as a neoadjuvant to chemotherapy, ICIs demonstrated comparable efficacy in terms of OS, PFS, and pCR. This benefit is offset by an increase in immune-related adverse events, such as hyperthyroidism, hypothyroidism, pneumonitis, and adrenal insufficiency. We also demonstrated that Atezolizumab is safer than Pembrolizumab in the neoadjuvant setting. Only trials evaluating early-stage TNBC showed a significant improvement in pCR, implying that PD-1/PD-L1 inhibitors may be most effective when started early in the disease course. Durvalumab as a maintenance therapy instead of chemotherapy in patients with metastatic TNBC has also shown promising results in terms of survival extension. Future research should focus on PD-L1 expression status and TNBC subtypes, as these parameters may aid in the optimization of personalized treatment regimens for TNBC patients.

Availability of data and materials

Data used in this study analysis is provided in the Additional file 1 : (Table S7). Further analysis data requests and inquiries can be directed to the corresponding author.

Abbreviations

Confidence interval

Combined positive score

Food and Drug Administration

Generalized pairwise modelling

Hazard ratio

• Immune checkpoint inhibitors

Intention-to-treat

• Network meta-analysis
• Pathological complete response

Programmed cell death protein 1

Programmed cell death ligand 1

Preferred reporting items for systematic reviews and meta-analyses

International prospective register of systematic reviews

Cochrane risk-of-bias tool 2

Surface under the cumulative ranking curve

Tumor infiltrating lymphocytes

Triple-negative breast cancer

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IE: Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing, illustration of tables and figures. RA: Data curation, Formal analysis, Writing—original draft, Writing—review and editing. AE: Data curation, Formal analysis, Writing—original draft, Writing—review and editing. AE: Data curation, Formal analysis, Writing—review and editing. MIM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Funding Acquisition, Writing—original draft, Writing—review and editing. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1: table s1:.

PRISMA checklist for this network meta-analysis. Table S2: Excluded articles at full-text screening. Table S3: Overall survival treatment ranking and surface under the cumulative ranking curve. Figure S1: Overall survival using generalized pairwise modelling. Table S4: Progression free survival treatment ranking and surface under the cumulative ranking curve. Figure S2: Progression free survival using generalized pairwise modelling. Table S5: Pathologic complete response treatment ranking and surface under the cumulative ranking curve. Figure S3: Pathologic complete response using generalized pairwise modelling. Table S6: Adverse events grade ≥ 3 treatment ranking and surface under the cumulative Table. Figure S4: Adrenal insufficiency odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S5: Diarrhea odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S6: Hyperthyroidism odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S7: Hypothyroidism odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S8: Infusion reaction odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S9: Pneumonitis odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S10: Anemia odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S11: Colitis odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S12: Fatigue odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S13: Nausea odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S14: Neutropenia odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S15: Rash odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S16: Vomiting odds network meta-analysis results. A Schematic diagram showing the network map for the treatments included in the analysis. B Rankogram showing the ranking probabilities for the least odds of causing this adverse event for each treatment. C Forest plot showing each trial effect size and confidence interval as well as the pooled effect size. D Bias-adjusted funnel plot showing each treatment separately. Figure S17: Adverse events grade ≥ 3 using generalized pairwise modelling. Table S7: Extracted data used for the analysis.

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Elmakaty, I., Abdo, R., Elsabagh, A. et al. Comparative efficacy and safety of PD-1/PD-L1 inhibitors in triple negative breast cancer: a systematic review and network meta-analysis of randomized controlled trials. Cancer Cell Int 23 , 90 (2023). https://doi.org/10.1186/s12935-023-02941-7

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A Systematic Literature Review of Human Error and Machine Error in Accident Investigation

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• Nathan B. Rowland Miller 6  

Part of the book series: Automation, Collaboration, & E-Services ((ACES,volume 11))

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The presence of error in systems has been an essential part of analyzing accidents and incidents when investigations occur. More importantly, it is imperative to look at the overall impacts of human error and machine error systems and how they impact the investigation and accident. For this study of the effects and implications of both, literature reviews were conducted, and tools like Purdue Databases, MAXQDA, Vicintas, VOSviewer, and Harzing's Publish or Perish were used to gather data. These tools analyzed the most prevalent articles in the study through co-citation analysis, and significant trends were mapped and tracked. From this, the conclusion is found that the relationship between human error and machine error is dependent on one another, as the presence of one can cause the other. Additionally, in this analysis, consideration was given when looking into the relationship that machine AI and accidents and errors have. The conclusion reached is that while a connection is evident between the two points and there are systems in the works, these systems need to be implemented in a more robust fashion and with more stability in this practice.

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Rowland Miller, N.B. (2023). A Systematic Literature Review of Human Error and Machine Error in Accident Investigation. In: Duffy, V.G., Landry, S.J., Lee, J.D., Stanton, N. (eds) Human-Automation Interaction. Automation, Collaboration, & E-Services, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-10784-9_18

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Association of cigarette smoking habits with the risk of prostate cancer: a systematic review and meta-analysis

• Xiangwei Yang 1   na1 ,
• Hong Chen 2   na1 ,
• Shiqiang Zhang 1 ,
• Xianju Chen 1 ,
• Yiyu Sheng 1 &
• Jun Pang 1  

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Association of cigarette smoking habits with the risk of prostate cancer is still a matter of debate. This systematic review and meta-analysis aimed to assess the association between cigarette smoking and prostate cancer risk.

We conducted a systematic search on PubMed, Embase, Cochrane Library, and Web of Science without language or time restrictions on June 11, 2022. Literature search and study screening were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Prospective cohort studies that assessed the association between cigarette smoking habits and the risk of prostate cancer were included. Quality assessment was conducted using the Newcastle–Ottawa Scale. We used random-effects models to obtain pooled estimates and the corresponding 95% confidence intervals.

A total of 7296 publications were screened, of which 44 cohort studies were identified for qualitative analysis; 39 articles comprising 3 296 398 participants and 130 924 cases were selected for further meta-analysis. Current smoking had a significantly reduced risk of prostate cancer (RR, 0.74; 95% CI, 0.68–0.80; P  < 0.001), especially in studies completed in the prostate-specific antigen screening era. Compared to former smokers, current smokers had a significant lower risk of PCa (RR, 0.70; 95% CI, 0.65–0.75; P  < 0.001). Ever smoking showed no association with prostate cancer risk in overall analyses (RR, 0.96; 95% CI, 0.93–1.00; P  = 0.074), but an increased risk of prostate cancer in the pre-prostate-specific antigen screening era (RR, 1.05; 95% CI, 1.00–1.10; P  = 0.046) and a lower risk of prostate cancer in the prostate-specific antigen screening era (RR, 0.95; 95% CI, 0.91–0.99; P  = 0.011) were observed. Former smoking did not show any association with the risk of prostate cancer.

Conclusions

The findings suggest that the lower risk of prostate cancer in smokers can probably be attributed to their poor adherence to cancer screening and the occurrence of deadly smoking-related diseases, and we should take measures to help smokers to be more compliant with early cancer screening and to quit smoking.

Trial registration

This study was registered on PROSPERO (CRD42022326464).

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Prostate cancer (PCa) is the second most commonly diagnosed cancer and the fifth leading cause of cancer death among males, with an estimated 1.4 million new cases and 375 000 deaths worldwide in 2020, accounting for 7.3% and 3.8% of all cancers diagnosed, respectively [ 1 ]. Various endogenous and exogenous risk factors for PCa have been discussed for decades. Several factors have been identified to be associated with an increased risk of PCa, for instance, family history [ 2 ], elevated hormone levels [ 2 ], black ethnicity [ 2 ], and high alcohol consumption [ 3 ]. Conversely, several factors have been associated with a decreased risk of PCa, such as higher intake of tomatoes [ 4 ], increased coffee consumption [ 5 ] and sexual activity [ 6 ].

Smoking is a well-established risk factor for several cancers, such as lung cancer, head and neck cancer, bladder cancer, and esophageal cancer [ 7 , 8 ]. However, the data on the association between smoking and PCa incidence are conflicting [ 9 , 10 ]. In a meta-analysis of 24 prospective cohort studies [ 11 ], M. Huncharek showed that current smokers had no increased risk of incident PCa, but in data stratified by amount smoked, a significant elevated risk was observed, and former smokers had a higher risk of PCa in comparison with never smokers. Another meta-analysis conducted in 2014 [ 12 ] revealed an inverse association between current smoking and PCa risk, while in studies completed before the prostate-specific antigen (PSA) screening era, ever smoking was positively associated with PCa. In addition, a recent pooled study of five Swedish cohorts [ 13 ] demonstrated that former smokers and current smokers had a lower risk of PCa than never smokers, and smoking intensity was inversely associated with PCa risk, especially in the PSA screening era.

Biological mechanisms underlying smoking and PCa risk have been studied for many years. Burning cigarettes can produce more than 7000 chemicals, and at least 70 carcinogens such as polycyclic aromatic hydrocarbons (PAHs) and cadmium [ 14 ]. Mutations or functional polymorphism in genes involved in PAH metabolism and detoxification may increase the risk of PCa [ 15 ]. The glutathione-S-transferases (GSTs) are a class of enzymes that can detoxify PAHs. The most common subtypes of GSTs in human prostate are GSTP and GSTM, which were reported to be associated with an increased risk of PCa in smokers [ 15 , 16 ]. Cadmium induces prostate carcinogenesis through interaction with the androgen receptor because of its androgen-like activity, and it also enhances androgen-mediated transcriptional activity when in combination with the androgen [ 17 ]. A higher level of androgen was related to increased PCa risk [ 2 , 18 ]. Smoking can increase testosterone concentrations by promoting testosterone secretion from Leydig cells or acting as an aromatase inhibitor [ 19 ]. Mutations in the p53 gene and CYP1A1 gene showed a higher risk of PCa in smokers, suggesting that smoking may have a joint effect on PCa risk when combined with susceptible genotypes [ 20 ]. Increased heme oxygenase 1 (HO-1) messenger RNA expression and upregulated HO-1 protein levels were observed in PCa cell lines DU 145 and PC3 [ 21 ], implying that HO-1 may play a role in the development of PCa for its function in promoting angiogenesis [ 22 ]. Evidence also suggested that prostatic inflammation may be involved in the development and progression of PCa [ 23 ]. Cigarette smoke augments the production of numerous pro-inflammatory cytokines, decreases the levels of anti-inflammatory cytokines, and activates macrophage and dendritic cell activity in many ways [ 24 ].

We performed this systematic review and meta-analysis to investigate the association of cigarette smoking habits with the risk of PCa. We aimed to include a larger sample of studies than previous meta-analyses and collect the latest evidence and the most comprehensive information on the association between cigarette smoking and PCa risk. Our primary objective was to assess the risk of PCa in current smokers, former smokers, and ever smokers. We hypothesized that smokers have a higher risk of PCa compared to non-smokers.

Search strategy

This systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 25 ]. Two independent investigators (XWY and HC) searched PubMed, Embase, Cochrane Library, and Web of Science for publications from database inception to June 11, 2022. The following search terms were used: ("Prostate cancer") AND ("Cigarette" OR "Smoking" OR "Tobacco") AND ("Risk" OR "Incidence"). No language restrictions were applied. Reference lists of identified articles and relevant reviews were screened for additional studies. Details of the protocol for this systematic review were registered on PROSPERO and can be accessed by CRD42022326464.

Selection criteria

Prospective cohort studies investigating the association between cigarette smoking and PCa risk were included for analysis. The primary outcome was the risk of PCa. Those studies that provided an effect measure (i.e., a relative risk) quantifying the impact of smoking on the risk of PCa were considered for further quantitative synthesis (meta-analysis). The removal of duplicates and assessment of article eligibility were conducted independently by XWY and HC, and any disagreements were resolved by consulting the senior author (JP). Review articles, editorials, meeting abstracts, case‒control studies, cross-sectional studies, and those not on the topic were excluded.

Quality assessment

All included studies were independently assessed by XWY and HC for risk of bias using the Newcastle‒Ottawa Scale for cohort studies [ 26 ]. This scale assesses the selection of the study groups, the comparability of the groups, and the ascertainment of the outcome of interest. Studies with 7–9 scores were considered to be of high quality, those with 5–6 scores were classified as intermediate quality, and those with less than 4 scores were classified as low quality. Disagreements in the quality assessment were resolved by consulting JP.

Data extraction

Data were independently extracted by XWY and HC. All extracted variables were cross-checked to ensure their reliability. We recorded the total number of participants, PCa cases, and the mean or median follow-up time across all included studies. Relative risks (RRs) and the corresponding 95% confidence intervals (CIs) were retrieved or calculated using frequency distributions. Considering the prevalence rate of PCa in the public, we believed that the odds ratio was close to the RR [ 27 , 28 ]. Hazard ratios (HRs) and RRs are different, HRs contain temporal information but RRs do not [ 28 ]. We converted HRs to RRs based on the formula provided by Shor E et al. [ 29 ], and the corresponding 95% CIs were converted using the same method. RRs and 95% CIs of ever smokers were computed by combining the results for former and current smokers when these results were not reported in the original papers. In addition, we recorded the baseline characteristics, methods, adjusted confounding factors, and other important comments to establish comparability. Discrepancies were discussed and resolved by consensus.

Statistical analysis

Three authors (SZQ, XJC and YYS) performed statistical analyses using Stata software, version 16.0 (StataCorp). When both crude and adjusted RRs were provided, we used the most fully adjusted value. We calculated the pooled RRs and 95% CIs and plotted forest plots using random-effects models (DerSimonian‒Laird method) for the association of current smoking, former smoking, and ever smoking with the risk of PCa [ 30 ]. Statistical heterogeneity across the trials was assessed using the I 2 statistic and the Cochran’s Q test. Values of the I 2 statistic of approximately 25%, 50%, and 75% were interpreted as low, moderate, and high heterogeneity, respectively [ 31 ]. In the case of low heterogeneity, a fixed-effects model (Inverse variance method) was applied. We plotted funnel plots and used Egger’s test to examine publication bias. Additionally, a series of sensitivity analyses were performed to assess the robustness of our results. We stratified studies by reference status (never smoker, former smokers), completion year (pre-PSA screening era vs. PSA screening era), world region (North America vs. Europe vs. Asia vs. Australia), and the Newcastle‒Ottawa Scale score (≤ 6 points vs. > 6 points). We considered 1995 as a cutoff year of study completion to distinguish studies before and after the PSA screening era [ 12 ]. All tests were two-tailed, and P  < 0.05 was considered statistically significant.

Study population

We identified 7296 citations, and after removing duplicates, 4963 citations remained for screening. After the removal of ineligible citations, we retained 60 articles that we assessed for eligibility by reading the full text; 16 of these were excluded for specific reasons. Finally, 44 studies met our inclusion criteria for qualitative synthesis and meta-analysis (Fig.  1 ). The number of participants and PCa cases from each selected study for systematic review ranged separately from 997 to 844 455 and 54 to 40 821, with a median of 22 677 and 382, respectively. Overall, 39 studies with 3 296 398 participants and 130 924 cases were identified for meta-analysis, and 5 studies with 91 377 participants and 1364 cases were not included in meta-analysis due to lack of information (Additional file 1 ). Articles were published between 1989 and 2022 and were from studies conducted in the following geographic regions: 19 from Europe (4 from the United Kingdom, 4 from Norway, 3 from Sweden, 2 from Finland, 1 from France, 1 from the Netherlands, 1 from Denmark, 1 from Lithuania, 1 from Iceland, and one from 10 European countries), 18 from North America (17 from the United States, 1 from Canada), 5 from Asia (3 from Japan, 1 from South Korea, 1 from Singapore), and 2 from Australia. The median score of quality assessment for all eligible studies was 7, with a range of 6–9 (Additional file 2 ).

Flow diagram of included studies

Current smoking

In total, 37 studies [ 6 , 13 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] reported the risk of current smoking on PCa, among which 6 studies [ 32 , 35 , 41 , 53 , 55 , 63 ] took non-smokers as the reference and the remaining 31 studies [ 6 , 13 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 ] took never smokers as the reference. We defined non-smokers as never smokers plus former smokers. RRs and 95% CIs of current smokers versus non-smokers were calculated using frequency distributions in never smokers and former smokers when the risk estimates were not provided in original studies. Ten studies [ 34 , 36 , 38 , 39 , 42 , 43 , 54 , 58 , 59 , 66 ] did not provide enough data on frequency distribution and were not included in analysis. Twenty-seven studies [ 6 , 13 , 32 , 33 , 35 , 37 , 40 , 41 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 55 , 56 , 57 , 60 , 61 , 62 , 63 , 64 , 65 ] were included to calculate the pooled RR and 95% CI. The results showed that current smoking at baseline was associated with a reduced risk of PCa (RR, 0.74; 95% CI, 0.68–0.80; P  < 0.001) (Fig.  2 ). The I 2 statistic and the Cochran’s Q test showed high heterogeneity (I 2  = 90.5%; P  < 0.001). Inspection of the funnel plot did not demonstrate publication bias ( P  = 0.231; Fig.  3 ).

Forest plot for the association between current smoking and prostate cancer. RR, relative risk; CI, confidence interval; PCa, prostate cancer; US, United States; UK, United Kingdom. a Rohrmann et al. [ 49 ] had two sub-populations. b RR and 95% CI were calculated using frequency distributions. c RR and 95% CI were converted from HR and corresponding 95% CI using the formula RR ≈ (1-e HR x ln (1−P0) )/P 0 (P 0 refers to the incidence rate of PCa in the control group). d Weights were from random effects analysis

Funnel plot for publication bias in the studies investigating current smoking and prostate cancer risk. SE, standard error. Twenty-eight dots from 27 studies. P  = 0.231

When performing sensitivity analyses (Additional file 3 ) stratified by reference status, studies using never smokers as the reference [ 6 , 13 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 ] showed a similar inverse association with PCa risk (RR, 0.90; 95% CI, 0.86–0.95; P  < 0.001), with the heterogeneity lower than that of analysis of studies using non-smokers as the reference (I 2  = 66.7%; P  < 0.001). Compared to former smokers, current smokers had a significant lower risk of PCa (RR, 0.70; 95% CI, 0.65–0.75; P  < 0.001) based on 21 studies [ 6 , 13 , 33 , 37 , 40 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 56 , 57 , 60 , 61 , 62 , 64 , 65 ]. In the pre-PSA screening era, current smoking showed a decreased risk of PCa (RR, 0.79; 95% CI, 0.64–0.98; P = 0.033) compared to non-smokers, while in the PSA screening era, the risk was significantly lower (RR, 0.72; 95% CI, 0.66–0.79; P  < 0.001). When stratified by world region, studies conducted in North America, Europe, Asia, and Australia showed a negative association between current smoking and PCa risk. We also performed subgroup analyses in 21 studies with quality scores ≥ 7 [ 6 , 13 , 32 , 33 , 35 , 40 , 44 , 45 , 46 , 49 , 50 , 51 , 52 , 55 , 56 , 57 , 60 , 61 , 62 , 64 , 65 ] and 6 studies with quality scores of 6 [ 37 , 41 , 47 , 48 , 53 , 63 ]. Thereupon, both demonstrated a reduced risk of PCa.

Former smoking

Meta-analysis on former smoking as a risk factor for PCa was performed in 31 studies (Fig.  4 ) [ 6 , 13 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 ], and the results showed no significant association between former smoking and the risk of PCa (RR, 0.98; 95% CI, 0.95–1.02; P  = 0.313). The data were heterogeneous according to the I 2 statistic and the Cochran’s Q test (I 2  = 61.5%; P  < 0.001). Inspection of the corresponding funnel plot did not show evidence of publication bias ( P  = 0.431; Fig.  5 ). Sensitivity analyses stratified by PSA screening era, world region, and quality score also demonstrated no association between former smoking and PCa risk (Additional file 3 ).

Forest plot for the association between former smoking and prostate cancer. a Rohrmann et al. [ 49 ] had two sub-populations. b RR and 95% CI were calculated using frequency distributions. c RR and 95% CI were converted from HR and corresponding 95% CI using the formula RR ≈ (1-e HR x ln (1−P0) )/P 0 (P 0 refers to the incidence rate of PCa in the control group). d Weights were from random effects analysis

Funnel plot for publication bias in the studies investigating former smoking and prostate cancer risk. Thirty-two dots from 31 studies. P  = 0.431

Ever smoking

Thirty-three studies were included in the meta-analysis to assess the association of ever smoking with the risk of PCa (Fig.  6 ) [ 6 , 13 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 ]. Two of those studies [ 67 , 68 ] provided RRs and 95% CIs in the original paper, and the risk estimates of the remaining 31 studies [ 6 , 13 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 ] were calculated by combining results for former and current smokers. Thereupon, the pooled RR and 95% CI showed no association with the risk of PCa (RR, 0.96; 95% CI, 0.93–1.00; P  = 0.074), with an I 2 value of 67.0% and a negative result of publication bias (( P  = 0.672; Fig.  7 ). The association was inverse when analyzing studies completed in the PSA screening era (RR, 0.95; 95% CI, 0.91–0.99; P  = 0.011), but in the pre-PSA screening era, ever smokers showed a significantly increased risk of PCa compared to never smokers (RR, 1.05; 95% CI, 1.00–1.10; P  = 0.046) (Additional file 3 ). Four studies [ 50 , 57 , 60 , 67 ] from Asia showed a pooled reduced risk of PCa in ever smokers (RR, 0.82; 95% CI, 0.74–0.91; P  < 0.001), and studies from North America, Europe, and Australia revealed no association between ever smoking and PCa incidence. In terms of subgroup analyses stratified by quality score, the studies with a quality score ≥ 7 showed a modest negative association with PCa risk (RR, 0.96; 95% CI, 0.92–1.00; P  = 0.047), while the studies with a quality score of 6 showed no association.

Forest plot for the association between ever smoking and prostate cancer. DM, diabetes mellitus. a Rohrmann et al. [ 49 ] had two sub-populations. b RR and 95% CI were calculated using frequency distributions or risk estimates and 95% CIs in subgroups. c RR and 95% CI were converted from HR and corresponding 95% CI using the formula RR ≈ (1-e HR x ln (1−P0) )/P 0 (P 0 refers to the incidence rate of PCa in the control group). d Weights were from random effects analysis. e Onitilo et al. [ 68 ] had two sub-populations

Funnel plot for publication bias in the studies investigating ever smoking and prostate cancer risk. Thirty-five dots from 33 studies. P  = 0.672

Studies not included in the meta-analysis

Of these 5 studies (Additional file 1 ) [ 69 , 70 , 71 , 72 , 73 ], 4 studies (involving 211 cases, 524 cases, 127 cases, and 129 cases) [ 69 , 70 , 72 , 73 ] reported no significant association between cigarette smoking and the risk of PCa, 2 of which had a smoking category increment of 10 cigarette per day [ 69 ] or cigarette pack-years per 10 years [ 72 ]. The study conducted by Karlsen et al. [ 73 ] did not differentiate cigarette, cigar, cheroot, and pipe when assessing the risk of PCa in smokers, and as a result, this study could not be included in the meta-analysis. In the study conducted by Chamie et al. [ 71 ], a reduced PCa risk was reported in participants with a smoking history (with 13,144 participants and 363 cases; RR, 0.78; 95% CI, 0.72–0.85; P  < 0.001).

In this systematic review and meta-analysis, we found that current smoking was inversely associated with the risk of PCa, especially in the PSA screening era, which was inconsistent with our hypothesis but was consistent with the results of the recent studies [ 12 , 13 ]. In studies using never smokers as the reference, current smoking revealed a similar negative correlation with PCa risk, accompanied by less heterogeneity. Current smokers had a lower risk of PCa compared to former smokers. Former smoking and ever smoking were not associated with PCa risk in the overall analyses. However, when stratified by completion year, ever smoking showed an increased risk of PCa in the pre-PSA screening era and a lower risk of PCa in the PSA screening era. Studies from North America, Europe, Asia, and Australia showed a similar reduced PCa risk in current smokers compared to non-smokers, whereas in ever smokers, only studies conducted in Asia demonstrated a decreased risk of PCa. There are several explanations for these results. Current smoking was believed to be associated with a lower likelihood of PSA testing [ 74 , 75 ], and individuals with a smoking history were less likely to undergo prostate biopsy [ 62 , 76 ]. As a consequence, the detection rate of PCa could be relatively lower among participants in the PSA screening era. The difference in the patterns of the association between ever smoking and PCa risk in Asia and other regions can be attributed to the higher proportion of studies in the PSA screening era in Asia than afterward. Additionally, the differences in race/ethnicity, socioeconomic status, educational attainment, and health literacy may also play important roles in explaining regional distinctions [ 77 , 78 , 79 ]. In a national cross-sectional survey, PSA testing was significantly higher in US-born men and older non-Hispanic White men than in foreign-born men and men from other racial categories [ 77 ]. Another study revealed that White men aged > 50 years were more likely than Black men to undergo PSA testing, and those with lower socioeconomic status were associated with less PSA testing [ 78 ]. The association of education levels with the preference for PSA screening was inconsistent [ 77 , 79 ]. Johnson JA et al. [ 77 ] declared that higher educational levels were associated with higher odds of ever having had a PSA test; however, Pickles K et al. [ 79 ] announced that the preference for PSA screening was stronger in those without tertiary education and with inadequate health literacy. The age of the participants in the selected studies varied widely, and therefore, the willingness to receive PSA screening differs considerably; older people often show poorer adherence to PSA testing guidelines [ 77 ]. On the other hand, the relationship between PSA levels and smoking is still a matter of debate. According to an Italian cross-sectional study [ 80 ], PSA accuracy was reported to be lower in smokers than in nonsmokers and former smokers, suggesting that the need for PSA-based prostate biopsy can be affected to a certain extent by smoking.

Another possible explanation is that smoking is the leading risk factor for death among males [ 81 ]. Smokers may die from smoking attributable diseases including cancers, cardiovascular diseases, and respiratory diseases before their diagnosis of PCa. The majority of cases of lung cancer [ 7 ], head and neck cancer [ 82 ], approximately 50% of bladder cancer cases [ 83 ], and 49% of esophageal squamous cell carcinoma cases [ 84 ] are caused by cigarette smoking. Furthermore, smoking was reported to cause nearly 90% of lung cancer deaths [ 7 ] and showed significant associations with poor survival in patients with head and neck cancer [ 85 ]. Moreover, the detection of asymptomatic PCa can be frequently ignored when focusing on a more aggressive cancer. In addition, smoking increases the risk for stroke and coronary heart disease by 2 to 4 times, and stroke and coronary heart disease are considered to be the leading causes of death in the United States [ 8 ], and most of these deaths are caused by smoking [ 86 ]. Smoking can also cause chronic obstructive pulmonary disease (COPD), increasing 12 to 13-fold risk of dying from COPD than nonsmokers [ 8 ], and nearly 80% of deaths from COPD can be ascribed to smoking [ 86 ].

Our study found an increased risk of PCa among ever smokers in the pre-PSA screening era, indicating that it is necessary to promote smoking cessation as early as possible. Nearly one in five deaths are caused by cigarette smoking in the United States, leading to more than 480 000 deaths each year [ 8 ]. Continued tobacco use has been shown to limit the effectiveness of major cancer treatments, increase the risk of treatment-related complications and the development of secondary cancers, and lower cancer survival rates and the quality of life of patients [ 7 ]. In patients with PCa, smokers at the time of PCa diagnosis are associated with more aggressive characteristics, and the risk of experiencing biochemical recurrence, distant metastasis, cancer-specific mortality, and overall mortality is much higher [ 9 , 10 , 12 , 87 , 88 ]. Nicotine-induced chronic prostatic inflammation [ 23 , 89 ], aberrant CpG methylations of adenomatous polyposis coli and glutathione S-transferase pi are the potential biological mechanisms responsible for these [ 90 ]. Although the effect of smoking cessation on PCa progression remains unclear, the negative impact of smoking has suggested to be maintained as long as 10 years after smoking cessation [ 10 ]. Additionally, active smoking is associated with adverse reproductive health outcomes, type 2 diabetes mellitus, and rheumatoid arthritis, harming nearly every organ of the body and resulting in significant economic costs for smokers, their families, and society [ 7 ].

Much progress has been made in promoting smoking cessation in recent decades. However, it is far from sufficient. In 2018, 13.7% of all adults (34.2 million people) in the United States were reported as current cigarette smokers [ 91 ]. Of them, 55.1% had made an attempt to quit in the past 12 months, but only 7.5% achieved success. Overcoming both physical nicotine dependence and long-standing rewarding behavior is a huge challenge, and most individuals relapse within 3 months after quitting smoking [ 92 ]. Evidence has indicated that the combination of behavioral and pharmacological interventions produces the largest cessation effects [ 7 , 8 , 92 ]. Nevertheless, fewer than one-half of tobacco users were offered cessation treatment according to a survey of oncology providers [ 93 ], and the inability to get patients to quit and patient resistance to treatment are two dominant barriers to cessation intervention. A brief intervention may be more acceptable and sustainable to help smokers quit smoking, according to a randomized clinical trial performed at emergency departments in Hong Kong [ 94 ]. Quitlines are good alternatives to interventions for both patients and clinicians because of their convenience and specialization, and their roles in improving smoking cessation rates have been confirmed [ 95 ]. For smokers with time constraints, internet-based self-help materials such as the website smokefred.gov and newer smartphone applications have also shown benefits in promoting smoking cessation and can serve as good alternatives [ 96 , 97 ].

Strengths and limitations

The key strength of this systematic review is that the study comprised a total of 44 prospective cohort studies, 39 of which were included in the meta-analysis, with the largest number of participants and PCa cases to date. Furthermore, we included all the data on current smoking, former smoking, and ever smoking in the analysis without date and language restrictions, which means that the study provides the latest evidence and the most comprehensive information on the association between cigarette smoking and risk of PCa. We assessed the quality of each selected study using the Newcastle‒Ottawa Scale for cohort studies, and the median score was 7 and the lowest score was 6, suggesting that the quality of the included studies can be guaranteed. Other strengths include applying independent literature search, quality assessment, and data extraction by two investigators; conducting several sensitivity analyses; and using Egger’s test to examine publication bias.There are some limitations of our study. Most of the information on smoking habits was obtained from self-administered questionnaires, and the definitions of current smokers and former smokers were not completely the same between different studies. Some participants may have changed their smoking habits after baseline investigations, but repeated assessment of smoking exposure was absent in primary studies. We calculated RRs and the corresponding 95% CIs using frequency distributions without adjusting confounding factors when risk estimates were not reported. We focused on the impact of cigarette smoking on the risk of PCa; second-hand cigarette smoke and the use of other tobacco products (cigars, smokeless tobacco, e-cigarettes, pipes, etc.) that have showed increased risk of many cancers in numerous studies [ 98 , 99 ] were not discussed. Alcohol consumption showed a significant dose–response relationship with PCa risk in several studies [ 3 , 100 ], and were often used concurrently with cigarette smoking [ 101 ], but we didn’t analyze the effect of concurrent use of cigarette and alcohol on risk of PCa due to lack of information on alcohol consumption in the included studies. High heterogeneity was showed by the I 2 statistics and the Cochran’s Q test, and the difference in adjusted confounding factors may be one of the reasons. We have included multivariate results as much as possible to reduce the bias, and there was no indication of publication bias. Dividing studies into pre-PSA screening era and PSA screening era based on publication year (1995 as the cut-off) may produce bias because many of the cohorts published and categorized into the PSA screening era extended into the pre-PSA screening era. Another limitation is that we failed to calculate the impact of quantitative cigarette consumption on the PCa risk due to a lack of data. However, we have to point out that the meta-regression conducted by Islami et al. [ 12 ] was methodologically wrong as including multiple data points from a single study with the same control group counts the effect of that control group multiple times (i.e., unit-of-analysis error).

To the best of our knowledge, this systematic review and meta-analysis contained the largest sample of prospective cohort studies, the latest evidence and the most comprehensive information on the association between cigarette smoking habits and the risk of PCa. The smokers’ poor adherence to cancer screening and the occurrence of smoking-related aggressive cancers as well as cardiovascular, pulmonary, and several other deadly diseases may explain the negative association. Regional distinctions can be attributed to the difference of participants in age, ethnicity, socioeconomic status, and educational levels. In addition, a correct methodology is important, the choice of different effect models should base on the heterogeneity and characteristics of enrolled studies. However, it is difficult to conclude a positive association between cigarette smoking and PCa risk as we hypothesized due to these affecting factors. We should focus on taking measures to help smokers to be more compliant with early cancer screening and to quit smoking.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

• Prostate cancer

Prostate-specific antigen

Polycyclic aromatic hydrocarbons

Glutathione-S-transferases

Heme oxygenase 1

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Cigarettes per day

Confidence interval

Relative risk

Not reported

United States

Blood pressure

Body mass index

United Kingdom

Diabetes mellitus

Non-significant

Chronic obstructive pulmonary disease

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Acknowledgements

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This work was supported by the Sanming Project of Medicine in Shenzhen (grant number: SZSM202011011). The funder had no role in the study design, data collection, analysis and interpretation, or writing of the report.

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Xiangwei Yang and Hong Chen contributed equally to this work.

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Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, No.628 Zhenyuan Road, Shenzhen, 518107, China

Xiangwei Yang, Shiqiang Zhang, Xianju Chen, Yiyu Sheng & Jun Pang

School of Nursing, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China

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XWY, HC and JP conceptualized the study and developed the registered protocol for the review. XWY and HC conducted the literature search, quality assessment, data extraction, and drafted the manuscript. SQZ, XJC and YYS performed statistical analyses. JP revised the manuscript, obtained funding and supervised the project. JP is responsible for the overall content and serves as the guarantor. All authors helped refine the final version of the manuscript and approve with its submission.

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Supplementary Information

Additional file 1..

A. Characteristics of the 39 studies included in the meta-analysis. B. Characteristics of the 5 studies not included in the meta-analysis due to lack of information.

Additional file 2.

 Results of quality assessment using the Newcastle-Ottawa Scale for cohort studies.

Additional file 3.

 Sensitivity analyses of association between smoking status and risk of prostate cancer.

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Yang, X., Chen, H., Zhang, S. et al. Association of cigarette smoking habits with the risk of prostate cancer: a systematic review and meta-analysis. BMC Public Health 23 , 1150 (2023). https://doi.org/10.1186/s12889-023-16085-w

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Received : 21 November 2022

Accepted : 09 June 2023

Published : 15 June 2023

DOI : https://doi.org/10.1186/s12889-023-16085-w

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