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• Published: 03 April 2019

Prognosis and improved outcomes in major depression: a review

• Christoph Kraus   ORCID: orcid.org/0000-0002-7144-2282 1 , 2 ,
• Bashkim Kadriu   ORCID: orcid.org/0000-0002-3809-9451 2 ,
• Rupert Lanzenberger   ORCID: orcid.org/0000-0003-4641-9539 1 ,
• Carlos A. Zarate Jr. 2 &
• Siegfried Kasper   ORCID: orcid.org/0000-0001-8278-191X 1  

Translational Psychiatry volume  9 , Article number:  127 ( 2019 ) Cite this article

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Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.

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Depression: a major and relentless burden

Major depressive disorder (MDD) is the most common psychiatric disease and a worldwide leading cause of years lived with disability 1 , 2 . In addition, the bulk of suicides are linked to a diagnosis of MDD. Despite the high prevalence rate of MDD and ongoing efforts to increase knowledge and skills for healthcare providers, the illness remains both underdiagnosed and undertreated 3 . Many novel strategies with potentially broad impact are not yet ready for ‘prime time’, as they are either in early experimental stages or undergoing regulatory processes for approval. This review sought to: (1) provide a synopsis of key factors associated with outcomes in MDD, and (2) synthesize the existing literature on novel treatment strategies for depression. A literature search was conducted using the search terms ‘depression’, ‘antidepressant’, ‘outcome’, ‘predictor’, ‘(bio)marker’, ‘treatment-resistant depression (TRD)’, and ‘chronic depression’ in addition to combinations of these terms. The search was conducted in PubMed, Scopus, and Google Scholar with no restrictions on time period and concluded in October 2018. Notably, we defined ‘outcomes’ loosely, as either disease course (i.e., treatment resistance, chronic depression) or response/remission to treatment.

Prognostic variables for treatment outcomes in MDD

Clinical variables.

Clear evidence of an inverse relationship between duration of episode and treatment outcome (either response or remission) underscores the importance of early intervention in MDD 4 (Table 1 ). In particular, replicable prospective and retrospective studies indicate that shorter duration of untreated disease—both in terms of first and recurrent episodes—is a prognostic factor indicating better treatment response and better long-term outcomes 5 , 6 , 7 , 8 , 9 , 10 , although not all studies have found such an association 11 . Another important clinical variable is time to antidepressant response. For instance, one meta-analysis found that early improvement was positively linked to antidepressant treatment outcome in 15 of 16 studies 9 . Early response to antidepressant treatment appears to occur independently of treatment modality 12 , 13 or outcome parameters 14 , 15 . Another study found that early improvement in work productivity was a significant positive predictor of higher remission rates after three and seven months of treatment 16 . Similarly, imaging studies found that early response to treatment correlated with default mode network deactivation in the posterior cingulate 17 , as well as thickening of gray matter in the anterior cingulate cortex (ACC) 18 . Interestingly, two recent meta-analyses found that initial improvement was linked to response and outcome but failed to be associated with treatment resistance 19 , 20 . This suggests that TRD—defined loosely here as non-response to at least two adequate antidepressant trials—and chronic depression (roughly defined here as non-response to any treatment) may have similar response slopes in the earliest treatment stages.

In addition, lower baseline function and quality of life—including longer duration of the current index episode—have been associated with lower remission rates to various types of antidepressant treatments 21 , 22 . This is in line with results from a previous study that found that baseline function predicted antidepressant response in TRD patients 23 . Worse outcomes in more severely ill patients at baseline were also reported in elderly patients treated in primary-care settings 24 . In contrast, several controlled clinical studies found that elevated baseline severity correlated with improved response and remission rates 25 . Two naturalistic studies with broad inclusion criteria similarly found that remission correlated with higher baseline scores 4 , 26 . However, this discrepancy might be explained by variations in outcome according to parameter. It was noted earlier that studies that defined remission as percent change of baseline values might be biased in favor of higher baseline scores, while absolute endpoints (e.g., remission defined below a cutoff score) favor less sick patients 4 .

Psychosocial variables

The influence of sociodemographic factors such as age, age of onset, gender, and number of previous episodes on treatment outcome has been investigated with mixed results 4 , 27 , 28 . One study found that females had higher remission rates 21 , but this was not confirmed by another prospective study 27 . Others have found that stress related to high occupational levels might impair outcomes 29 . The European “Group for the Study of Resistant Depression” (GSRD) multi-site study found that age at first treatment (i.e., early-onset and early treatment), age, timespan between first and last episode (i.e., duration of illness), suicidality, and education level were all important variables for outcome 30 . Notably, authors of long-lasting longitudinal studies have suggested that recall bias may influence the age of onset variable 31 , 32 ; given the cognitive deficits associated with acute episodes of MDD, retrospective studies must hence address the factor of memory bias in data collection.

Environmental stress and stressful life events (SLEs)

High stress levels significantly influence outcomes in MDD patients who are prone to vulnerable states, such as those with high levels of neuroticism 33 , 34 . A meta-analysis found that history of childhood maltreatment was associated with elevated risk of developing recurrent and persistent depressive episodes, as well as with lack of response or remission during treatment 35 . Another meta-analysis confirmed the detrimental impact of childhood maltreatment (emotional physical or sexual maltreatment or neglect) as a predisposing risk factor for severe, early-onset, and treatment-resistant depression 36 , 37 . Studies also found gender-specific effects; in particular, at lower stress levels females were at higher risk of MDD than males 34 . Moreover, twin studies have suggested a differential reactivity of gender in response to type of SLE 38 . For instance, a treatment study using escitalopram and nortriptyline investigated the association between number of SLEs (e.g., job loss, psychological trauma, loss of a loved one) and antidepressant treatment. Subjects with more SLEs exhibited greater cognitive symptoms at baseline but not significantly more mood or neurovegetative symptoms. These patients also had greater cognitive symptom reduction in response to escitalopram but not nortriptyline 39 . This suggests that SLEs may have a cognitive domain-specific impact in MDD, but more data are needed to elucidate this issue.

Psychiatric and physical comorbidities

Psychiatric comorbidity has been shown to influence outcome in both treated and untreated patients 40 , 41 . Studies have found that elevated baseline anxiety symptoms or comorbid anxiety disorder are associated with worse antidepressant response to first-line selective serotonin reuptake inhibitors (SSRIs) or second-line treatment strategies 42 , 43 . Worse outcomes have also been reported for MDD patients with comorbid drug or alcohol use disorders, post-traumatic stress disorder (PTSD), and “double depression” (depression and dysthymia) 26 , 41 . Data from the Sequential Treatment Alternatives to Relieve Depression (STAR*D) study, which included patients who were seeking medical care in routine medical or psychiatric outpatient treatment, indicate that roughly one-third (34.8%) of all MDD patients are free of any comorbidity; the most frequent comorbid Axis-I disorders are social phobia (31.3%), generalized anxiety disorder (23.6%), PTSD (20.6%), and obsessive-compulsive disorder (14.3%) 21 . A large recent study found that clinically diagnosed personality disorder was associated with negative outcomes (with regard to remission and persistent depressive symptoms) six months after diagnosis in MDD subjects enrolled in primary care 44 . Moreover, meta-analytic studies indicate that comorbid personality disorder increases the likelihood of poorer outcomes 45 , 46 ; it should be noted, though, that negative studies have also been reported 40 .

MDD and several physical diseases—including cardiovascular disease and diabetes—appear to have bidirectional effects on disease trajectory 47 , 48 , yet pathophysiologic links are most likely complex and have to be elucidated. In addition, depression appears to be linked to hormonal diseases, including hypothyroidism 49 . A number of physical disabilities and medical comorbidities have been shown to significantly impact outcome measures in MDD 50 , particularly in elderly subjects 51 . This connection appears to be relevant at any stage of the disease, as number of physical comorbidities did not separate TRD from non-TRD patients 52 . Links between MDD and pain have also been noted; subjects with elevated levels of baseline pain due to chronic conditions had longer depressive episodes, delayed remission 53 and, most importantly, elevated suicide risk 54 , 55 . Interestingly, a prospective, 12-month study of older patients found that elderly patients with atrial fibrillation exhibited better remission rates 56 . Patients with chronic pulmonary diseases had worse outcomes in uncontrolled treatment settings than those without these diseases. This difference was absent in the intervention group, in which depression care managers helped physicians with guideline-concordant recommendations and helped patients adhere to treatment 56 . Further longitudinal studies on shared pathophysiology with physical diseases are needed to confirm such associations.

Neuroimaging markers of treatment outcomes

Structural markers of antidepressant treatment outcomes suggest that hippocampal volumes are related to response and remission 57 , 58 . One study found that low baseline hippocampal volumes were related to impaired treatment outcomes after 3 years 59 ; a meta-analysis confirmed that low baseline hippocampal volumes are associated with negative outcomes 60 . However, negative studies have also been reported 61 , 62 . The volume of other brain regions, including the anterior cingulate or orbitofrontal cortices, have also been shown to be decreased in MDD subjects 63 , but more longitudinal neuroimaging trials with antidepressants are needed to clarify this association. Interestingly, several studies, including one meta-analysis 64 , found significant hippocampal volume increases after ECT 65 , 66 , 67 , although the relationship to antidepressant response has yet to be confirmed 64 , 68 .

The largest functional magnetic resonance imaging (fMRI) study of MDD patients conducted to date reported neurophysiological subtypes based on connectivity patterns within limbic and frontostriatal brain areas 69 . In subset analyses, connectivity patterns plus subtype classifications predicted response to repetitive transcranial magnetic stimulation (rTMS) treatment with higher accuracy (89.6%) than clinical characteristics alone. Other task-based and resting-state fMRI studies found that ACC activity (including pregenual activity) predicted treatment response 70 , a finding corroborated by an expanded electroencephalography study 71 as well as a meta-analysis 60 . While these interesting results suggest that fMRI measures could ultimately help classify biological subtypes of depression, these methods are far from ready for clinical application and results will have to be reproduced. However, given its easy implementation and the short time needed to acquire measurements, fMRI appears to be a promising tool for identifying imaging biomarkers.

Positron emission tomography (PET) studies have identified altered serotonin-1A (5-HT 1A ) receptor and 5-HT transporter (SERT) binding potentials, an index of protein concentration, at baseline and in TRD patients 72 , 73 , 74 , 75 . Most of these results found reduced baseline SERT levels and elevated baseline 5-HT 1A heteroreceptors in MDD patients (depending on PET methodology for 5-HT 1A ); non-remitters had lower 5-HT 1A autoreceptor binding in the serotonergic raphe nuclei 75 , as well as lower SERT 76 . Reduced global 5-HT 1A receptor binding has also been observed after ECT 77 . High costs, technical and methodological challenges, lack of dedicated PET centers with 11 C-radiochemistry, small sample sizes, small effect sizes, and unclear cutoff values have heretofore prevented the broader clinical application of these tools in MDD compared to disorders such as Alzheimer’s and Parkinson’s disease. An earlier [ 18 F]FDG PET study of unmedicated MDD patients was consistent with the aforementioned fMRI results, demonstrating increased glucose turnover in the orbitofrontal and posterior cingulate cortices and amygdala and decreased turnover in the subgenual ACC and dorsolateral prefrontal cortex 78 . A later study corroborated these results and found that glucose turnover was differentially affected by cognitive behavioral therapy or venlafaxine 79 . Interestingly, several studies detected microglial activation by labeling translocator protein (TSPO) with PET, using TSPO radioligands like 18 F-FEPPA. Microglial activation is closely linked to brain tissue damage, traumatic brain injury, neuroinflammation, and increased metabolic demands. Increased TSPO binding in MDD patients has been observed in the ACC, insula, and prefrontal cortex 80 . In addition, TSPO binding has also been shown to positively correlate with length of illness and time without antidepressant treatment, and to negatively correlate with SSRI treatment 80 . Elevated TSPO levels in unmedicated, acutely ill MDD patients have now been reported in at least two independent datasets 81 , 82 . However, TSPO-positive MDD patients may reflect a specific subtype (i.e., associated with neuroinflammation) and may, thus, respond better to treatments that target neuroinflammation. For a graphical summary of these findings see Fig. 1 .

Imaging findings exhibiting unidirectional (left) relationships with outcome in MDD vs. bidirectional (right). fMRI, functional magnetic resonance imaging; PET, positron emission tomography; EEG electroencephalography; 5-HT1A, serotonin-1A receptor; SERT, serotonin transporter; MAO-A monoamine oxidase-A; BP ND , nondisplaceable binding potential; V T , volume of distribution

Blood-based markers of disease outcomes

Consistent with neuroinflammatory processes, elevated levels of C-reactive protein (CRP), tumor necrosis factor alpha (TNFα), and interleukin-6 (IL-6) have been reported in a subset of MDD patients. In particular, elevated levels of CRP, a well-established marker of increased proinflammatory state in blood, was shown to be associated with MDD and increased risk for psychological distress in cross-sectional samples of the general population 83 . A longitudinal study found that lower CRP levels were associated with quicker response to SSRIs, an association not observed for SSRI-bupropion combination therapy 84 . Interestingly, elevated CRP levels have been shown to be more pronounced in female versus male MDD patients 85 . Similar findings have been observed for IL-6 and TNFα. One meta-analysis found that all three were significantly elevated at baseline in MDD patients, but their treatment trajectories differed 86 ; IL-6 levels decreased with antidepressant treatment, but outcomes were indistinguishable. In the same meta-analysis, persistingly high TNFα levels identified TRD patients 86 . Notably, heterogeneity was high within the pooled studies. Another study noted that levels of acute phase protein complement C3 significantly differentiated between atypical and melancholic MDD subtypes 87 . MDD patients have also been shown to have altered levels of peripheral adipokines and bone inflammatory markers; these deficits were corrected with ketamine treatment 88 , 89 .

Given the importance of neuroplasticity in the pathophysiology and treatment of depression, interest has grown in studying brain-derived neurotrophic factor (BDNF), a neurotrophin involved in the structural adaptation of neuronal networks and a prerequisite for neuronal reactions to stressors. BDNF blood levels most likely stem from peripheral tissue. While these peripheral levels are linked to central levels, the question of whether BDNF is actively transported through the blood–brain barrier remains controversial 90 . Compelling evidence suggests that BDNF levels are decreased at baseline in MDD patients and elevated in response to pharmacological 90 , 91 treatments as well as ECT 92 . A meta-analysis found that increased BDNF levels in response to treatment successfully stratified responders and remitters compared to non-responders 93 .

Outcome and genetic and epigenetic links

Heritable risk for MDD is between 30 and 40%, with higher rates in women. A large, collaborative genome-wide association study (GWAS) detected 44 significant loci associated with MDD 94 . Specific analyses identified neuronal genes (but not microglia or astrocytes), gene-expression regulating genes (such as RBFOX1 ), genes involved in gene-splicing, as well as genes that are the targets of antidepressant treatment. The authors suggested that alternative splicing could lead to shifts in the proportion of isoforms and altered biological functions of these proteins 94 .

Hypothesis-driven approaches with candidate genes have provided initial insights into the influence of single-nucleotide polymorphisms (SNPs). It is beyond the scope of this manuscript to review the large number of candidate genes; here, we outline only several representative genes (see Table 1 for meta-analytic evidence of treatment outcomes). These include synaptic proteins involved in stress response, antidepressant binding structures, or neuroplasticity (e.g., CRH receptor 1 ( CRHR1 )), the sodium-dependent serotonin transporter ( SLC6A4 ), and BDNF 95 . The aforementioned multicenter GSRD study also found that combining clinical and genetic variables explained antidepressant response better than SNPs alone in a random forest algorithm 96 . In that study, regulatory proteins such as ZNF804A (associated with response) and CREB1 (associated with remission), as well as a cell adhesion molecule (CHL1, associated with lower risk of TRD), were linked to antidepressant treatment outcomes. Another interesting candidate gene is FK506 binding protein 5 ( FKBP5 ), which was found to moderate the influence of standard treatments in an algorithm lasting up to 14 weeks 97 ; FKBP5 is known to influence HPA axis reactivity 98 , treatment response 99 , and epigenetic mechanisms in response to environmental stressors 100 . Another relevant avenue of research is drug-drug interactions and gene isoforms in the cytochrome P450 pathway (CYP450), which could account for insufficient amounts of a given drug reaching the brain or, conversely, result in exceedingly high plasma values, making subjects more vulnerable to treatment side effects 101 , 102 . Several commercially available kits categorize patients according to their phenotypic status (e.g., CYP2D6, 2C19, CYP3A4). This led to the introduction of phenotype categories—“poor”, “intermediate”, “extensive (normal)”, and “ultrarapid” metabolizers—based on CYP450 isoenzyme status and their relationship to plasma levels at fixed doses 102 . A large naturalistic study of CYP2C19 isoforms found that treatment success with escitalopram was less frequent in “poor” (CYP2C19Null/Null) and “ultrarapid” metabolizers (CYP2C19*1/*17 or CYP2C19*17/*17) 103 .

Clinical subgroups, TRD, and treatment outcomes

While some studies have suggested that depressive subtypes in MDD—including anxious, mixed, melancholic, atypical, and psychotic depression—respond differently to antidepressant treatment, this literature is mixed. For instance, some studies found that melancholic patients initially present with high levels of severity and may respond less well to SSRI treatment than to venlafaxine or tricyclic antidepressants 104 , but other studies did not support this finding 105 . No association was found between subgroups and clinical outcomes in a parallel design, uncontrolled study investigating sertraline, citalopram, and venlafaxine 106 , which found that near equal percentages of patients who met criteria for a pure-form subtype (39%) also had more than one subtype (36%), making these psychopathological subtypes difficult to classify.

It should be noted that treatment success might have more discriminatory power for identifying subgroups than psychopathological subgroup stratification. Although a wide range of definitions exists specifying the number of failed trials necessary to diagnose TRD 107 , the core definition of TRD centers around a lack of improvement in response to consecutive, adequate antidepressant treatments. Resistance occurs at alarmingly high rates and is thought to affect 50–60% of all treated patients 107 . Unsurprisingly, this group of patients has dramatically worse outcomes than those who respond to antidepressants, and factors that are associated with TRD overlap with many of those presented above 28 . Cross-sectional data from the GSRD 108 identified a number of risk factors linked to TRD, including comorbidity (particularly anxiety and personality disorders), suicide risk, episode severity, number of hospitalizations, episode recurrence, early-onset, melancholic features, and non-response at first treatment 28 . Most importantly, TRD is life-threatening, and associated with a two- to threefold increased risk of suicide attempts compared to responding patients, and a 15-fold increased risk compared to the general population 109 . Taken together, the evidence indicates that TRD patients need special attention, as outcomes in these individuals are significantly worse.

Novel and existing strategies to improve treatment outcomes

Early identification, prevention, and early treatment.

Numerous programs for suicide prevention exist 110 , and recognizing acute depressive symptoms is just one of many important facets of such work. Screening tools for early identification of depressed patients can be helpful 111 , and such instruments can start with as few as two items—for instance, the Patient Health Questionnaire-2 112 or Ask Suicide-Screening Questions (asQ’em) 113 —and proceed to more detailed instruments if initial screens are positive. Positive screening should be followed by a diagnostic interview to determine whether patients meet criteria for MDD 111 . In the general population, two large independent studies that used only clinical variables were nevertheless able to accurately predict depression within 1–3 years 114 . In addition, long-term monitoring of vulnerable subjects with known SLEs may further improve the ability to identify at-risk individuals early in their course of illness. As noted above, duration of untreated disease is a negative predictor of treatment outcomes. Because the advantages of early intervention in MDD have been demonstrated 115 , efforts to achieve early treatment might also help slow disease progression in individuals with TRD; however, this hypothesis has not been sufficiently tested.

Modeling environmental impact on predisposition

As noted above, severe SLEs constitute an important risk factor. Elegantly designed studies have demonstrated that genetic predisposition, in concert with SLEs, might account for increased vulnerability to MDD 100 . In this manner, the presence of ‘weak alleles’ in candidate genes such as BDNF, SERT , and others would be increasingly detrimental in the presence of SLEs 116 , 117 . However, studies have been quite inconsistent and yielded small effect sizes, including a negative result in 252 patients enrolled in the GSRD study 118 . It should be noted that counter-regulatory mechanisms or resilience factors, such as social support, may exist that counter SLEs. Nevertheless, preliminary research suggests that the impact of SLEs on MDD may depend on measurable factors such as gender and the timing of exposure 119 . Both genes and the environment are complex systems with frequent opportunity for interaction and elaborate compensatory mechanisms. While the complexity of genetic susceptibility in MDD can be tackled through enormous collaborative projects 94 , the interactions between genetic susceptibility and environmental factors have yet to be determined. Properly powered gene×environment interaction projects may exceed current research capabilities, and large longitudinal studies will certainly be needed 120 .

Developing markers for subgroup identification and disease course

Pioneering research on biological differences—for instance, between patients with atypical versus melancholic depression—suggests differential HPA axis or autonomous nervous system reactivity 121 , 122 , though the subtype results have been only moderately consistent across time and are prone to low group specificity 123 , 124 , 125 . However, at least one study demonstrated the more reliable stability of extreme types over a 2-year period 87 . Interestingly, one study found that individuals with atypical depression had significantly higher body-mass index, waist circumference, levels of inflammatory markers, and triglyceride levels, and lower levels of high-density lipid cholesterol than those with melancholic depression or controls 126 . Using fMRI and biological variables, another study found that MDD subjects could be divided into low/high appetite groups with distinctive correlations between neuronal activity and endocrine, metabolic, and immune states 127 . Other research groups have tried to overcome conventional psychopathological subgroups and model biotypes using resting-state fMRI 69 . Molecular and functional neuroimaging, as well as epigenetic studies, are promising approaches for separating subgroups and may be better suited to identifying screening markers (see Fig. 2 ) that are exclusively valid in certain subgroups with higher predictive power.

These approaches highlight the feasibility of linking and stratifying psychopathological categories with biological variables, a goal further supported by the Research Domain Criteria (RDoc), which seek to link dimensions of observable behavior with neurobiological systems 128 . In the search for biomarkers, subgroup- or domain-specific classifications using unidimensional variables might improve subgroup stratification 129 . Moreover, applying markers to other categories could boost the utility of existing markers that have failed in any given category (see Fig. 2 for established markers). As a field, the focus is largely on staging and prediction markers, but ‘predisposition’ or ‘recurrence’ markers may equally be worth investigating. Presently, however, the relative lack of biologically defined MDD subgroups and their stratification are key obstacles to finding and establishing treament outcome predictors appropriate for broader clinical applications.

Candidate disease markers can be applied in clinically meaningful ways. While only candidate markers are presently available, sorting these according to their potential applications may facilitate the development of clinically applicable disease markers. The outline follows the classification of markers as suggested by others 200 (modified and reprinted with permission from Springer)

The most important outcome of successful subgroup stratification and staging markers would be that patients and their relatives would receive valuable information at treatment onset about how their disease is likely to improve or worsen. Toward this end, the development of staging methods provides promising solutions. Currently, at least five different methods exist 130 that, to date, have not been evaluated thoroughly enough for clinical implementation. Continuous variables—as obtained by the Maudsley Method and Massachusetts General Hospital Staging Model—appear to provide greater staging advantages than categorical variables. It should be noted here that data indicate that research in severely ill, suicidal, and TRD subjects is safe to conduct in controlled inpatient settings 131 . Presently, patients in various stages of disease and/or treatment history are lumped together and compared in statistical analyses. We propose that staging should be more thoroughly integrated into clinical trial design.

Algorithm- and guideline-based treatments

Despite the availability and distribution of a variety of expert-based guidelines, only a fraction of patients are actually treated according to guidelines 132 (see Table 2 for current guidelines (≤10 years)). New guidelines – particularly for TRD – and more rigorous implementation of guideline-based care are needed. Improvements in currently available treatments have been conducted using treatment algorithms and following sequential treatment strategies, with standardized instructions for therapeutic decision-making. In the past two decades, large, collaborative studies using treatment-based algorithms have introduced standardized, sequential treatments; these include the Texas Medication Algorithm Project 133 , the STAR*D trial 21 , and the German algorithm project 134 . Indeed, evidence suggests that algorithm-based treatments improve treatment outcomes 135 and are cost effective 136 . Here, we considered current clinical treatment guidelines to create a sequential treatment optimization scheme of recommended treatments. While there is no fixed time-frame, first- and second-line treatments are recommended sequentially during the first episode and within 3 months (see Fig. 3 , which also illustrates the need for more third- and fourth-stage treatment options). Figure 4 , illustrates potential reasons for “pseudoresistance” 42 that should be ruled out during this time-frame.

A sequential treatment optimization scheme was generated based on antidepressant treatment guidelines (see Table 2 ). Treatment optimization is possible for patients being treated for the first time but also for patients with insufficient response to first- or second-stage therapies. a Treatment response curves for four common types of patients highlight the importance of sequentially introducing the next step upon non-response to previous steps. b Currently available treatments are listed in neuroscience-based nomenclature 201 with treatment lines corresponding to improvement curves in a . Although current classifications vary, patients classified as having treatment-resistant depression (TRD) are eligible for second- or third-stage therapies. 5-HT1A and similar: serotonin receptor subtypes; DBS: deep brain stimulation; DAT: dopamine transporter; D2: dopamine receptor D2; ECT: electroconvulsive therapy; MAO: monoamine oxidase; NET: noradrenaline transporter; SERT: serotonin transporter; TBS: theta-burst stimulation; rTMS: repetitive transcranial magnetic stimulation; DA: dopamine; NE: norepinephrine.

Points—in random order—follow earlier suggestions by Dold and Kasper (2017) 202

Reducing placebo response in clinical trials while harnessing placebo effects in clinical treatment

The issue of placebo response in antidepressant trials has become increasingly important 137 , 138 . Indeed, the contribution of placebo effects to early response needs to be systematically studied in order to disentangle biological therapy-induced effects from psychologically induced effects. Strikingly, in the brain, anatomically similar regions that mediate placebo response are affected by MDD (for a comprehensive review, see ref. 139 ). Several mechanisms contribute to placebo response, including patients’ expectations of benefits, behavioral conditions, and the quality of patient-physician interactions 139 . Strategies for reducing placebo response could lead to better discrimination between effective treatments in clinical trials; such strategies include extending trial duration, excluding placebo responders by including a placebo run-in, or using randomized run-in and withdrawal periods 138 , 139 . Others have suggested using more thorough criteria to select study participants 140 . On the other hand, when antidepressant agents are used clinically, placebo effects must be taken advantage of by harnessing patients’ expectations and learning mechanisms to improve treatment outcomes 141 .

Novel antidepressant treatments

The recent discovery that glutamatergic-based drugs are uniquely capable of rapidly and robustly treating mood disorders has ushered in a new era in the quest to develop novel and effective antidepressants 142 , 143 , 144 . In this regard, the prototypic glutamatergic modulator ketamine has catalyzed research into new mechanistic approaches and offered hope for the development of novel, fast-acting antidepressants. While ketamine’s underlying mechanism of action remains the subject of active investigation, several theories have been propsed 144 . These include N-methyl- d -aspartate receptor (NMDAR)-dependent mechanisms, such as the inhibition of NMDARs on gamma aminobutyric acid (GABA)-ergic interneurons, the inhibition of spontaneous NMDAR-mediated transmission, the inhibition of extrasynaptic NMDARs, the inhibition of lateral habenula neurons, and GABA B receptor expression/function 144 . Substantial evidence also supports additional NMDAR-independent mechanisms, including the stabilization of glutamate release/excitatory transmission, active metabolites such as hydroxynorketamine, regulation of the dopaminergic system, G-alpha subunit translocation, and activation of cyclic adenosine monophosphate, as well as potential sigma-1 and mu-opioid receptor activation 145 . Among those theories, a leading hypothesis remains that NMDAR antagonism increases BDNF synthesis, a process mediated by decreased phosphorylation of eukaryotic elongation factor-2 and the subsequent activation of the mammalian target of rapamycin pathway by BDNF activation of the TrkB receptor 146 , 147 . These putative mechanisms of action are not mutually exclusive and may complement each other to induce potentiation of excitatory synapses in affective-regulating brain circuits, resulting in improved depressive symptoms.

The initial serendipitous discovery that a single, subanesthetic-dose ketamine infusion has rapid-acting antidepressant effects in MDD 148 , a finding subsequently confirmed by numerous randomized trials, has been hailed as one of the most important discoveries in psychiatry in the last decades 149 . The initial proof-of-concept studies demonstrated that a single dose of ketamine (0.5 mg/kg, IV) administered over 40 min led to rapid, robust, and relatively sustained antidepressant effects in TRD—both MDD 150 , 151 , 152 , 153 and bipolar depression 154 , 155 . In research settings, studies of TRD patients found response rates of >70% within 24 h post-infusion 153 , with about 50–70% of participants exhibiting a variable duration of response 156 , 157 . Ketamine has also been shown to be superior to any blinding counterpart 158 . Off-label ketamine use has also been associated with significant and rapid (one to four hours) antisuicidal effects 150 , 159 , 160 , a finding supported by a large, recent metanalysis showing that ketamine exerted rapid (within hours) and sustained (up to 7 days) improvements in suicidal thoughts compared to placebo 161 .

Esketamine hydrochloride

The ketamine enantiomer esketamine received approval by the FDA for TRD and is currently undergoing further Phase III clinical trials. A Phase II, 10-week, clinical trial of flexibly dosed intranasal esketamine (28 mg/56 mg or 84 mg) found that, in TRD patients, this agent demonstrated rapid and clinically relevant improvements in depressive symptoms compared to placebo 162 . Strikingly, 65% of TRD patients met response criteria through Day 57. In another Phase II proof-of-concept, multi-site, 4-week, double-blind study, standard treatment plus intranasal esketamine (84 mg) was compared to standard treatment plus placebo in individuals with MDD at imminent risk of suicide 163 . The authors found a rapid antisuicidal effect, as assessed via the Montgomery-Åsberg Depression Rating Scale Suicide Item score at 4 h.

Other rapid acting and novel antidepressants

Based on the success of ketamine, other rapid-acting or novel antidepressant substances within the glutamatergic/GABA neurotransmitter systems are being developed, several of which are in Phase III clinical trials. A prototype novel substance is AV-101 (L-4-cholorkynurenine). This is a potent selective antagonist at the glycine-binding site of the NMDAR NR1 subunit and has demonstrated antidepressant-like effects in animal models, while human Phase II studies are currently ongoing 164 . Brexanolone is a formulation of the endogenous neurosteroid allopregnanolone, which modulates neuronal activation of GABA A receptors and has met positive endpoints in Phase III, leading to FDA approval for postpartum depression. A comparable substance is under development for MDD 165 . In addition, serotonergic agonists have been studied as our understanding of their mechanism of action (e.g., their effects on glutamate release or plasticity) has increased 166 . Encouraging results have been seen for the serotonin 2A receptor agonist psilocybin 167 , but these findings need to be replicated in larger systematic clinical trials. Initial positive trials of add-on agents—such as buprenorphine 168 , 169 , rapastinel 170 , or scopolamine 145 —have also been conducted. However, it is beyond the scope of this manuscript to review all of these findings, and we refer the interested reader to recent comprehensive reviews of this subject 144 , 145 , 165 , 171 .

Transcranial stimulation paradigms

In contrast to pharmaceutical treatments that exert their efficacy at the molecular level, electrical stimulation techniques target entire neuronal circuits. TMS of the (left) dorsolateral prefrontal cortex has been FDA-approved since 2008 to treat depression in patients who failed to respond to one standard antidepressant treatment. Apart from transient local skin and muscle irritation at the stimulation site and headaches, it is a very safe technique with few side effects. Studies have repeatedly demonstrated the superiority of rTMS over sham procedures, though effect sizes have been moderate 172 , 173 , 174 . Initial studies suggest that rTMS is also effective in TRD but the data are too few to draw definitive conclusions 175 , 176 . Improvements in rTMS techniques known as theta-burst stimulation (TBS) provide significantly shortened treatment times (3 min for TBS versus 37 min for rTMS) and hence allow more patients to be treated per day. A large non-inferiority trial of 414 moderately resistant MDD patients found that TBS was at least as effective as rTMS in reducing depressive symptoms 177 .

Electroconvulsive therapy (ECT)

Regarded as the ‘gold standard’, ECT has been successfully used for many years to treat severe TRD and exhibits both relatively rapid and sustained onset of efficacy; approximately 50% of all patients reach response criteria at the third treatment, typically within 1 week. It is also one of the most effective antidepressant therapies 178 , yielding response rates of ~80%, remission rates of ~75% 179 , and antisuicidal effects 180 . Remission is achieved by about 30% of patients within six ECT sessions 179 . ECT also reduces the risk of readmission 181 and is likewise safe to use in depressed elderly subjects 182 . The side effects of ECT include intermediate disorientation, impaired learning, and retrograde amnesia, all of which usually resolve 183 . The optimal anatomic location of the stimulus electrodes is a topic of current debate 184 , 185 . Recent evidence suggests that all three methods for electrode placement (bifrontal, bitemporal, and unilateral) show clinically significant effects 186 . While no difference in cognitive side effects was observed, bitemporal placement should be considered the first-line choice for urgent clinical situations. Despite its clinical efficacy, ECT remains underutilized. Its use is declining 187 because it needs to be administered in hospital settings under anesthesia, and partly because of misleading portrayals of the procedure itself. Adjusting the dose of electrical stimuli (e.g., through refined electrode placement or individually adjusted pulse amplitudes) may improve ECT’s side effect profile.

Magnetic seizure therapy (MST)

MST uses high doses of rTMS to induce seizures 188 . The electromagnetically induced electrical field generated by MST is unifocal and variable, as there are individual differences in the degree to which the skull provides electrical resistance 189 . As an advantage over ECT, MST is associated with a more superficial stimulation, which exerts less impact on the medial-temporal lobe where cognitive side effects are thought to be elicited. To date, few research sites across the world have used MST, with a concomitant dearth of open-label trials. Nevertheless, the preliminary treatment data suggest that results obtained with MST are similar to those obtained with ECT but with a more favorable side effect profile 190 , 191 .

Vagus nerve stimulation (VNS)

VNS is a surgically implanted pacemaker-like device attached to a stimulating wire threaded along the left vagus nerve. Since 2005, the FDA has approved VNS use for the adjunctive long-term treatment of long-lasting recurrent depression in patients 18 years and older who are experiencing a major depressive episode and have failed to respond to four or more previous adequate standard antidepressant treatment trials. In such cases, it has been shown to have superior long-term effects over conventional psychopharmacological treatment 192 . A recent, large, observational, adjunctive, open-label, naturalistic study followed TRD patients over 5 years 193 . In this group, adjunctive VNS led to significantly better clinical outcomes and higher remission rates than treatment as usual (67.6% vs. 40.9%, respectively).

Deep-brain stimulation (DBS)

DBS involves the neurosurgical implantation of electrodes and has become clinically routine in the treatment of Parkinson’s disease and Dystonia. The technique is safe, removable, and does not cause lasting neuronal lesions. In TRD, anatomical targets include the subgenual cingulate, nucleus accumbens, habenula, and medial forebrain bundle. Clinical trials typically only enroll severely ill TRD patients whose current episode has lasted >12 months, whose age of onset is <45 years, and who have failed to respond to at least four adequate prior treatment trials of standard antidepressants, ECT, and/or psychotherapy. Initial open-label or single-blind trials found that DBS had both rapid and sustained antidepressant effects 194 , 195 , 196 . In contrast, one large and one smaller sham-controlled clinical study both failed to achieve their primary endpoints of symptom reduction 197 , 198 . To date, the number of MDD patients treated with DBS has been very small compared to other treatment options, including ECT and TMS. Nevertheless, brain-electrode interfaces are evolving quickly and it is possible that next generation brain-responsive stimulation devices will be able to adjust stimulation on-demand only when abnormal biological marker impulses (e.g., pulse amplitude) are detected 199 .

Conclusions

Although enormous progress has been made in measuring, predicting, and improving outcomes, depression remains a relentless disease that places a heavy burden on both individuals and society. The research reviewed above indicates that early recognition and early adequate treatment at illness onset are preferable to watch-and-wait strategies. The studies reviewed above also underscore the manner in which SLEs, as well as physical and psychiatric comorbidities, contribute to impaired outcomes. Together, these factors contribute toward treatment resistance, which has gained a substantial amount of importance as a patient-stratifying variable.

This paper also reviewed biological markers, where research has grown exponentially to encompass enormous projects with potentially tens of thousands of subjects enrolled in real world studies. In parallel, studies exploring the underlying genetics of depression have evolved from early candidate gene studies of neurotransmitters, stress, or gene-regulatory systems to large GWAS that help reveal potential new pathways and treatment targets. Moreover, the burgeoning field of proteomics has found promising target molecules. Nevertheless, despite the wealth of recent work in this area, no single biomarker has yet been used in clinical applications. A substantial need exists for replication and, because many biomarker studies are currently open-label, for controlled studies. In combination with neuroimaging techniques such as fMRI, genes or blood-based markers have a high potential of future implementation in stratification of MDD or serve as prognostic marker on treatment outcome.

Above, we also outlined efforts to optimize outcomes. We argue that disease-inherent heterogeneity, in concert with inaccurate group stratification tools, might have contributed to the lack of clinically applicable stratification and response prediction markers. Successful subgroup identification, and the ability to use this information in clinical settings, is crucial to improving future treatment paradigms. While recent research has increasingly focused on TRD, we wish to reiterate that no standard definition of TRD presently exists. Thus, based on currently available guidelines, we have outlined a sequential treatment optimization scheme that includes options for TRD; such work highlights the substantial need to develop and improve “third-line-and-beyond” therapeutics. In this context, this manuscript also reviews novel treatments and brain stimulation techniques that have demonstrated rapid antidepressant effects in TRD, including ketamine, esketamine, ECT, MST, TMS/TBS, VNS, DBS, and others. When treating TRD patients, physicians should consider illness severity, the chronicity of past and recent depressive episodes, the side effect profile of available treatment options, as well as previous refractoriness to particular treatment approaches. If acuity supersedes chronicity, one could consider fast-acting interventions such as ketamine or ECT/MST.

This review, though comprehensive, was not able to consider several lines of evidence on outcome prediction and treatment improvement. In particular, we focused on clinical outcomes in humans and were, thus, unable to fully explore the highly valuable advances made in translational science. Similarly, it was beyond the scope of this manuscript to review the richness of results from animal research and their relevance to MDD. Moreover, given the amount of literature, we were not able to incorporate many proteomic, genetic, or psychopharmacological findings.

Taken together, this review outlines important clinical, psychosocial, and biological factors associated with response and remission to antidepressant treatment (see Table 3 ). Recent studies have led to important insights into neurobiological disease markers that could result in improved disease stratification and response prediction in the near future. Key discoveries into novel rapid-acting substances, in concert with improvements in brain stimulation techniques, may also result in significantly improved treatment outcomes in formerly hard-to-treat patients.

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Acknowledgements

We thank the 7SE research unit and staff for their support. Ioline Henter (NIMH) provided invaluable editorial assistance. We also thank E. Acevedo-Diaz, Z.D. Deng, and J.W. Evans for scientific input.

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Christoph Kraus, Rupert Lanzenberger & Siegfried Kasper

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Funding for this work was supported in part by the Intramural Research Program at the National Institute of Mental Health, National Institutes of Health (IRP-NIMH-NIH; ZIA MH002927). All support given to authors was not related to the design of the manuscript or the ideas stated in this review. Dr. Kasper received grants/research support, consulting fees, and/or honoraria within the last 3 years from Angelini, AOP Orphan Pharmaceuticals AG, AstraZeneca, Eli Lilly, Janssen, KRKA-Pharma, Lundbeck, Neuraxpharm, Pfizer, Pierre Fabre, Schwabe, and Servier. Dr. Lanzenberger received travel grants and/or conference speaker honoraria from AstraZeneca, Lundbeck A/S, Dr. Willmar Schwabe GmbH, Orphan Pharmaceuticals AG, Janssen-Cilag Pharma GmbH, and Roche Austria GmbH. Dr. Kraus has received travel grants from Roche Austria GmbH and AOP Orphan. Dr. Zarate is a full-time U.S government employee. He is listed as a co-inventor on a patent for the use of ketamine in major depression and suicidal ideation; as a co-inventor on a patent for the use of (2 R ,6 R )-hydroxynorketamine, ( S )-dehydronorketamine, and other stereoisomeric dehydro and hydroxylated metabolites of ( R,S )-ketamine metabolites in the treatment of depression and neuropathic pain; and as a co-inventor on a patent application for the use of (2 R ,6 R )-hydroxynorketamine and (2 S ,6 S )-hydroxynorketamine in the treatment of depression, anxiety, anhedonia, suicidal ideation, and post-traumatic stress disorders. He has assigned his patent rights to the U.S. government but will share a percentage of any royalties that may be received by the government.

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Kraus, C., Kadriu, B., Lanzenberger, R. et al. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry 9 , 127 (2019). https://doi.org/10.1038/s41398-019-0460-3

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Depression in primary care: part 1—screening and diagnosis

• Related content
• Peer review
• Erin K Ferenchick , assistant clinical professor of medicine 1 ,
• Parashar Ramanuj , consultant psychiatrist , senior research fellow 2 3 ,
• Harold Alan Pincus , professor and vice chair , co-director , senior scientist 4 5 6
• 1 Center for Family and Community Medicine, Columbia University Medical Center, New York, NY, USA
• 2 Royal National Orthopaedic Hospital
• 3 RAND Europe
• 4 Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA
• 5 Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
• 6 RAND Corporation, Pittsburgh, PA, USA
• Correspondence to: E K Ferenchick ef2015{at}columbia.edu

Depression is a common and heterogeneous condition with a chronic and recurrent natural course that is frequently seen in the primary care setting. Primary care providers play a central role in managing depression and concurrent physical comorbidities, and they face challenges in diagnosing and treating the condition. In this two part series, we review the evidence available to help to guide primary care providers and practices to recognize and manage depression. In this first of two reviews, we outline an approach to screening and diagnosing depression in primary care that evaluates current evidence based guidelines and applies the recommendations to clinical practice. The second review presents an evidence based approach to the treatment of depression in primary care, detailing the recommended lifestyle, drug, and psychological interventions at the individual level. It also highlights strategies that are being adopted at an organizational level to manage depression more effectively in primary care.

Introduction

Depression is an important health problem often seen in primary care. More than eight million doctors’ visits each year in the US are for depression, and more than half of these are in the primary care setting. 1 Similarly, in the UK, more than a third of all visits with a general practitioner are estimated to involve a mental health component, with 90% of patients receiving treatment and care for their mental health solely in a primary care setting. 2 Depression is a broad and heterogeneous condition. In this article, we provide an epidemiologic framework for depression in primary care and review the current evidence on how to most effectively screen for and diagnose major depressive disorder in the primary care setting, acknowledging that efforts to identify and treat depression are limited without adequate support systems in place.

Sources and selection criteria

To collect and review the evidence available on depression in primary care, we searched the PubMed and Cochrane databases for relevant articles published between 1 January 2007 and 30 September 2017 by using medical subject headings (MeSH) terms including “depressive disorder”, “depression”, and “major depressive disorder” in combination with “primary care” and “general practice”. We did individualized searches for specific interventions thought relevant through the review process, including the addition of “screening” and “diagnosis” in combination with the search terms noted above. We limited our search to the English language. We reviewed titles and abstracts, excluding publications not relevant to primary care or for which major depressive disorder was not the main focus of the study.

We then used a pragmatic prioritizing framework to determine which studies to include in the review. This incorporated the level of evidence, with systematic reviews and meta-analyses given priority and interventional studies prioritized over observational studies; the recency, with more recent studies prioritized over older studies; and the sample size, with larger studies prioritized over those with smaller sample sizes. Where competing priorities existed—for example, a more recent smaller interventional study with similar objectives to a larger but older randomized controlled trial (RCT)—we assigned priority by consensus between the authors on the basis of methodology, study population, aims of the study, and outcome measures.

In addition, we searched references of included studies for further relevant studies. If a study had been conducted before our search timeframe of the past 10 years, we considered it for inclusion by virtue of its size or methodology or owing to absence of other research conducted in the area. We made reference to older, but noteworthy, validation studies as appropriate.

Epidemiology

The lifetime prevalence and course of depression vary across countries and regions, 3 but the overall high prevalence and persistence of major depression globally confirm the growing worldwide importance of this disorder. The World Health Organization (WHO) estimates that more than 300 million people, or 4.4% of the world’s population, have depression, and the total number of people living with depression increased by 18.4% between 2005 and 2015. 4 In 2015 in the US alone, 16.1 million adults reported at least one major depressive episode in the previous year, representing 6.7% of all US adults. 5 Similarly, in England, an estimated 4-10% of people will experience depression in their lifetime. 6

Depression is associated with a combination of genetic, environmental, biological, cultural, and psychological factors. Meta-analysis from six high quality family studies estimates the heritability of depression to be around 37% (95% confidence interval 31% to 42%). 7 This is much lower than for other mental disorders, 8 suggesting that most depression at a population level can be explained by environmental factors.

Depression can occur at any age, although it often begins in the second or third decade of life. 9 Prevalence rates vary by age, peaking in older adulthood, with an estimated prevalence of 7.5% among women and 5.5% among men aged 55-74 years. 4 Population studies have consistently shown major depression to be about twice as common in women as in men. 10 11 Differing patterns of depression exist within racial and ethnic minorities and are attributable to nativity, socioeconomic status, and the interplay between other protective effects and risk factors. 12 13 14

Depression as a comorbidity

Depression has an important bidirectional relation with other chronic physical diseases. These conditions are often worse when depression is present, and depression and chronic conditions have a joint effect on functional disability. 15 A large population based study among 245 404 people in 60 countries found that 9-23% of people with one or more chronic physical conditions experienced comorbid depression, compared with 3.2% (3.0% to 3.5%) of people who experienced depression in the absence of other physical conditions. 16 A 2007 population based study using data on 30 801 adults from the National Health Interview Survey analyzed the prevalence and odds of major depression and the incremental effect of major depression on health resource utilization, lost productivity, and functional disability in people with common chronic medical disorders. Results included 12 month prevalence and age/sex adjusted odds of major depression by chronic conditions and were as follows: congestive heart failure 7.9% (odds ratio 1.96), hypertension 8.0% (2.00), diabetes 9.3% (1.96), coronary artery disease 9.3% (2.30), chronic obstructive pulmonary disease 15.4% (3.21), and end stage renal disease 17.0% (3.56). 17 Similarly, neurological disorders also carry a high risk of depression. A 2018 study from the Neurological Institute of the Cleveland Clinic sought to estimate the prevalence of depression among patients with epilepsy, stroke, and multiple sclerosis. Analysis of 23 000 visits involving 7946 patients in neurology specialty clinics showed an overall point prevalence of depression of 29%. For stroke, epilepsy, and multiple sclerosis, prevalence of depression was 23% (21% to 25%), 33% (31% to 35%), and 29% (28% to 30%), respectively. 18

In addition, prescription drugs taken for common chronic medical conditions may cause side effects that contribute to depression. This has been confirmed by multiple studies, including by a recent large cross sectional survey study with 26 192 participants. Adults who were concurrently using three or more drugs that had the potential to cause depression as an adverse effect were more likely to report concurrent depression (8.5%, 5.0% to 12.0%) than those not using any of them (4.5%, 4.0% to 5.0%). 19

Personal and economic cost of depression

Depression is associated with increased morbidity and mortality and has a major effect on quality of life for patients and their families. 20 21 Depression has become the leading cause of disability worldwide, 4 resulting in severe impairments that limit the ability to carry out activities of daily living. Depressive disorders are ranked globally as the single largest contributor to non-fatal health loss (7.5% of all years lived with disability). 4 In the US, depression accounts for 3.7% of all disability adjusted life years and 8.3% of all years lived with disability. 5 People who have depression miss work because of illness at a rate twice that of the general population. 22 The financial burden of depression is substantial and rising. Although little research has been done to assess the overall economic burden of depression in Europe, the cost of depression was estimated at £9 billion in England in 2000. 23 In the US, the incremental economic burden of people with major depression was $173.2 billion in 2005 and climbed to $210.5 billion in 2010, an increase of 21.5% over this period. A large portion of this increase was attributable to higher direct medical costs and presenteeism, defined as being present but not fully functional in the workplace. 24

Screening for depression in primary care

The challenges associated with correctly recognizing depression in primary care have been widely documented and represent a combination of patient, provider, and system related barriers. Importantly, the likelihood of depression being diagnosed correctly is reduced when patients present with somatic complaints. 25 26 Although some patients with depression may present with the classic symptoms of depressed mood, many patients will report nonspecific symptoms or only somatic complaints in primary care settings. These frequently include changes in appetite, lack of energy, sleep disturbance, general aches and pains, back problems, severe headaches, menstrual symptoms, digestive problems, abdominal pain, and sexual dysfunction. Older patients are less likely to report low mood and often present with only physical complaints or a deterioration in cognitive ability. 27 Similarly, some ethnic minorities are more likely to present with nonspecific somatic symptoms. 28 29 If they mention it at all, patients will often wait until the end of the primary care consultation to share any concerns about depressed mood. 30 Thus, primary care providers must consider depression in patients on the basis of a composite profile of common somatic complaints and depressed mood, as well as the presence of any known risk factors for depression, 31 which are summarized in box 1 .

Risk factors for depression

Previous episode of depression

History of other mental illness

History of substance use

Family history of depression or suicide

Chronic medical illness 32

Unemployment 33

Poor social support systems 34

Recent stressful life event that includes loss 35

Intimate partner’s violence 36

Recognizing depression in the primary care setting, particularly in patients with multiple comorbidities, can be difficult. Thus, screening with self reported questionnaires has emerged as an approach to aid primary care providers in identifying patients who may have depression but who do not yet have a diagnosis.

What do guidelines recommend?

Clinical practice guidelines on screening for depression in primary care differ between countries, and general consensus has not been reached. This can present a challenge for primary care providers as they implement screening practices.

In the US, several recommendations support general screening for depression in primary care. The US Preventive Services Task Force (USPSTF) recommends screening for depression in the general adult population, including pregnant and postpartum women, when an adequate system is in place to ensure accurate diagnosis, effective treatment, and appropriate follow-up. 37 Similarly, the American Academy of Family Physicians and American College of Preventive Medicine both advocate screening in the general adult population, whereas the Institute for Clinical Systems Improvement recommends that providers screen for depression if it is suspected on the basis of risk factors or presentation. 38

By comparison, the UK National Screening Committee (UKNSC) does not recommend screening the general population, and the National Institute for Health and Care Excellence (NICE) guidelines in the UK suggest screening only in those patients with a previous history of depression or significant physical illness. 39 The Canadian Task Force on Preventive Health Care (CTFPHC) had previously endorsed screening in primary care, but in 2013 it changed its recommendation to no longer recommend routine screening for depression in adults who are at average risk of depression or in subgroups of the population who may be at increased risk. 40

A review published in 2017 compared the guidelines on screening for depression recommended by three leading organizations (CTFPHC, UKNSC, and USPSTF) for consistency and sources of divergence. The authors found that when recommendations diverged, the USPSTF expressed confidence in the benefits of general screening based on indirect evidence, posited potential harms as minimal, and did not consider cost or resource use. By contrast, they noted that the CTFPHC’s and UKNSC’s recommendations against general depression screening focused on the lack of direct evidence of benefit and expressed concerns about both resource use and possible harm to patients, including overdiagnosis and overtreatment. 41

Current evidence on screening and how it applies to practice

Recommendations for or against screening should ideally be based on high quality RCTs to assess the effectiveness of the screening itself. A 2005 systematic review of 12 RCTs of the administration of case finding/screening instruments for depression in non-mental health settings found that the routine use of depression screening questionnaires had little effect on the detection of depression (relative risk 1.00, 95% confidence interval 0.89 to 1.13). The intervention rates for those providers who received the screening results were variable, and the overall impact of screening on the management of depression was characterized as minimal (relative risk 1.35, 0.98 to 1.85). 42 Similarly, a more recent 2013 pragmatic, cluster randomized trial of 3737 people evaluated the effectiveness of general screening in everyday clinical practice on recognition of depression. No significant differences were seen between recognition rates for the screening intervention group versus the control group (58% v 48.1%; odds ratio 1.40, 0.73 to 2.68). However, suboptimal adherence to implementation of screening throughout the study period may have partially explained the results. 43

Large prospective cohort studies have also illustrated attempts to screen without benefit. In particular, a 2009 study evaluated the effectiveness of screening on initiation of treatment among 2005 participants in three high risk groups in primary care in the Netherlands—patients with mental health problems, patients with unexplained somatic complaints, and patients who frequently consult their general practitioner. As a final result of the screening, only 17 patients (1% of the 1687 screened) started treatment for major depressive disorder. Screening for depression in high risk populations was thus deemed to be ineffective. The authors attributed this mainly to low rates of treatment initiation. 44

The current evidence suggests that screening alone is not an effective strategy to improve the quality and outcomes of care, but we believe that screening in primary care carries important benefit when primary care practices can support accurate diagnosis, effective treatment, and appropriate follow-up. Systems based interventions that complement screening are necessary for the adequate treatment of depression, just as they are for other chronic diseases. A controlled implementation trial in 1924 participants in four non-metropolitan primary care networks compared collaborative care versus screening and follow-up for patients with diabetes and depressive symptoms. It found that patients had greater 12 month improvements in the Patient Health Questionnaire (PHQ) (mean 7.3 (SD 5.6) compared with 5.2 (5.7) in active controls; P=0.015). Recovery of depressive symptoms (that is, PHQ reduced by 50%) was also greater among intervention patients (61% v 44%; P=0.03). Compared with trial patients, non-screened controls had significantly less improvement at 12 months in the PHQ score (3.2 (SD 4.9)) and lower rates of recovery (24%) (P<0.05 for both). 45

For primary care practices that wish to screen for depression, there is little evidence available on the optimal timing for screening, nor on the optimum screening interval. We recommend following the guidance of the USPSTF, 37 which promotes a pragmatic approach in the absence of data, including screening all adults who have not been screened previously and using clinical judgment in consideration of risk factors, comorbid conditions, and life events to determine whether additional screening of patients at high risk is warranted.

Although recognizing depression may be a process that often takes more than a single consultation and emerges in a context of ongoing building of trust, validated instruments can help primary care providers to identify and routinely monitor patients with depression. A wide range of screening instruments with variability in style, length, validity, and feasibility for the primary care setting is available. A 2002 systematic review found that median sensitivity across 16 instruments for major depression was 85%, ranging from 50% to 97%, whereas median specificity was 74%, ranging from 51% to 98%. 46 A more recent 2018 review examining the psychometric properties of 55 tools or adaptions used for depression screening found an even greater range of sensitivity and specificity values from 28% to 100% and from 43% to 100%, respectively. 47

Screening tools

The general consensus is that the PHQ, including both the two item (PHQ-2) and nine item (PHQ-9) questionnaires, meets the criteria for a good screening tool in its validity, reliability, and brevity. Additionally, the PHQ is free and available in the public domain. The diagnostic validity of the PHQ-9 was established almost two decades ago, 48 and evidence continues to support its use today. Findings from a meta-analysis of 17 validation studies showed the acceptability of the PHQ-9 in a range of settings, countries, and populations. Across 14 studies with 5026 participants, the PHQ-9 had a sensitivity of 80% (95% confidence interval 71% to 87%) and a specificity of 92% (88% to 95%). The positive likelihood ratio was 10.12 (6.52 to 15.67), and the negative likelihood ratio was 0.22 (0.15 to 0.32). The PHQ-2, however, was validated in only three studies and showed a wider variability in sensitivity. 49

Additional studies have further attempted to examine the diagnostic properties of ultra-short screening instruments, including the PHQ-2, as a more practical and efficient option for overburdened and resource constrained primary care practices. A 2007 pooled analysis and meta-analysis of 22 studies evaluated the accuracy of one, two, three, and four item screening instruments for depression in primary care. Pooled analysis of single question tests showed an overall sensitivity of 32.0% and specificity of 97.0%. A one question test identified only three out of every 10 patients with depression in primary care. For two and three item tests, overall sensitivity on pooled analysis was 73.7% and specificity was 74.7%, with a positive predictive value of only 38.3% but a pooled negative predictive value of 93.0%. Ultra-short two or three question tests performed better, identifying eight out of 10 cases. However, the false positive rate was high, with only four out of 10 cases with a positive score actually diagnosed as having depression. 50

A more recent large validation study of 2642 participants in primary care practices in Auckland, New Zealand, compared both the PHQ-2 and PHQ-9 with a standard reference interview. The PHQ-2 was found to have a sensitivity of 86% and 61% for threshold scores of ≥2 and ≥3 and specificity of 78% and 92%, respectively. The positive predictive value was 21% for a threshold score of ≥2 and 34% for ≥3. The negative predictive value was 1.2% and 2.7%, respectively. For the PHQ-9, sensitivity and specificity were 74% and 91%, respectively, with a score of ≥10. 51

Two step screening

Primary care providers thus face the challenge of identifying the optimal screening approach for their practice. Many have chosen to adopt a two step screening process, first screening with the PHQ-2 and then confirming with the PHQ-9. A 2011 prospective cohort study of 1024 participants evaluated the two step screening recommendation from the American Heart Association for identifying depression in patients with cardiovascular disease to assess the accuracy and prognostic value. Findings showed that this screening method had high specificity (91%, 89% to 93%) but low sensitivity (52%, 46% to 59%) for diagnosing depression. Participants who screened positive on the stepped depression protocol had a 55% greater risk of events than did those who screened negative (age adjusted hazard ratio 1.55, 1.21 to 1.97; P<0.001). 52 Results from a more recent study of 562 patients with heart failure who were assessed for depression by using the two step method, however, showed no clear advantages of a stepped method compared with the two item screen alone or the nine item screen alone for predicting adverse prognostic effects of depressive symptoms. 53 Given the limited and conflicting evidence on the effectiveness of this stepped approach, additional research is needed to guide clinical practice.

“Help” question

The utility of adding of a “help” question to screening tools has also been explored. A cross sectional validation study of 1025 patients showed that adding a single question about desire for treatment (for example, “Is this something with which you would like help?”) resulted in similar sensitivities but improved both the diagnostic specificity and patient centeredness of depression screening. 54 The authors had previously found about five false positive responses for every true positive response for a two item screening test 55 ; however, in this study, the ratio changed from 4.3 to 1.5 when patients responded to either screening question plus the help question. A cohort study of 937 patients in a primary care setting in Switzerland has challenged these findings. The sensitivity and specificity of the two question method alone were 91.3% (81.4% to 96.4%) and 65.0% (61.2% to 68.6%), respectively. Adding the “help” question improved the specificity to 88.2% (85.4% to 90.5%) but decreased the sensitivity to 59.4% (47.0% to 70.9%). 56 Although additional studies are needed to further understand the potential benefits of a “help” question as a screening approach, there is likely value in using it to enable ongoing discussions and treatment planning in the primary care setting.

With this in mind, it is important to underscore that screening instruments should be used only to enhance, not replace, the clinical interview. Likewise, they should not be used in isolation for diagnostic purposes. The primary care provider should obtain a complete history of current illness, previous medical history, family history, current drug treatment, and any psychosocial stressors and do a focused physical examination.

Diagnosis of depression in primary care

A positive screen for depressive symptoms should trigger an additional diagnostic assessment. Two classification systems are widely used—the Diagnostic and Statistical Manual of Mental Disorder , 5th edition (DSM-5), developed by the American Psychiatric Association, and the International Statistical Classification of Diseases and Related Health Problems, 11th revision (ICD-11), developed and recently updated by WHO.

To date, no research has compared the effectiveness of DSM-5 and ICD-11 in diagnosing depression. However, the coexistence of DSM-IV and ICD-10 more generally has been much discussed, 57 58 and one isolated study focused on the differences between the two systems specifically in diagnosing depression. A 2010 study using item response theory to evaluate the DSM-IV and ICD-10 criteria for depressive disorders in 353 people in Japan found slight differences in the severity of depressive disorders between the DSM-IV and ICD-10 diagnostic criteria. ICD-10 was found to be more sensitive to the mild range of the depression spectrum than DSM-IV. Although some variations in severity existed among respondents, most of the respondents diagnosed as having a severe or moderate depressive episode according to the ICD-10 criteria were also diagnosed as having a major depressive episode according to DSM-IV. 59 In the absence of overwhelming evidence supporting one classification system over another, we recommend that either may be used for diagnosing depression in the primary care setting.

Although this review focuses on major depressive disorder, depression is a broad and heterogeneous condition. DSM-5 draws a distinction between a range of eight depressive conditions including disruptive mood dysregulation disorder, major depressive disorder, persistent depressive disorder (dysthymia), premenstrual dysphoric disorder, substance/medication induced depressive disorder, depressive disorder due to another medical condition, other specified depressive disorder, and unspecified depressive disorder. Similarly, the ICD-11 classification of depressive disorders includes single episode depressive disorder, recurrent depressive disorder, dysthymic disorder, mixed depressive and anxiety disorder, premenstrual dysphoric disorder, other specified depressive disorder, and unspecified depressive disorder. The common feature shared by these depressive disorders across both classification systems is the presence of sad or empty mood accompanied by somatic and cognitive changes that affect a person’s ability to function, but they are distinct in their duration, timing, and cause.

Formal diagnosis and assessment of severity

A formal diagnosis of major depressive disorder using the DSM-5 criteria requires at least one key symptom (low mood, loss of interest and pleasure, or loss of energy) to be present, whereas the ICD-11 criteria require depressed mood or diminished interest in activities to diagnose a depressive episode. In both, symptoms should be present for at least two weeks and each symptom should be present at sufficient severity for most of every day. DSM-5 requires at least five out of nine symptoms for a diagnosis of depression, whereas the updated ICD-11 classification system does not quantify the number of symptoms needed.

The severity of a patient’s depression should be primarily assessed on the basis of the degree of functional impairment, taking symptom severity into account, rather than being based solely on symptom count. Although this approach makes the grading of severity more subjective, highlighting the distinction is important as evidence based treatment is guided by severity. Both systems classify depressive episodes as mild, moderate, or severe on the basis of the number, type, and severity of symptoms present and degree of functional impairment. Figure 1 summarizes the two classification systems.

DSM-5 and ICD-11 classification systems

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Alternate psychiatric diagnoses

Depressive symptoms that do not meet criteria for major depressive disorder in DSM-5 or a single episode or recurrent depressive disorder in ICD-11 may be from other depressive disorders. The classification criteria should be consulted directly for a full description of each depressive disorder to assist the primary care provider in making a distinction and, thus, an accurate diagnosis.

Bipolar disorder

Any patient who presents with symptoms of depression should also be evaluated for possible bipolar disorder. Patients with bipolar disorder are often misdiagnosed as having major depressive disorder, particularly at initial presentation in the primary care setting, as several studies have found that more than a third of these patients remain misdiagnosed for up to 10 years. 60 61 Although patients with bipolar disorder are more likely to present with low mood rather than mania or hypomania, it is important to elicit any history of abnormally elevated or irritable mood, with persistent increased energy and a noticeable change from baseline behavior (DSM-5).

A 2013 systematic review of seven cross sectional studies measuring prevalence of bipolar disorder in primary care patients with depression or other psychiatric complaints found, through structured interviews, that bipolar disorder likely occurs in 3.4-9.0% of primary care patients with depression, exposure to trauma, medically unexplained symptoms, or a psychiatric complaint. Interestingly, by comparison, 20.9-30.8% of patients had positive results in studies that used screening measures, 62 suggesting a high false positive screening rate and limited role for screening for bipolar disorder in the primary care setting. However, as the misdiagnosis of bipolar disorder as major depressive disorder can lead to inappropriate treatment with antidepressants instead of a mood stabilizer, which may induce mania or rapid cycling, 63 future research is needed on how to diagnose bipolar disorder accurately in primary care patients presenting with depressive symptoms.

Comorbid psychiatric conditions

Patients with depression may also have comorbid psychiatric conditions, particularly anxiety disorders and substance use disorders (SUD), and the presence of one disorder significantly increases the likelihood that another disorder may also be present. 64 In the STAR*D trial, 53.2% of 2876 patients had anxious depression, defined as having a baseline 17 item Hamilton Rating Scale for Depression (HAM-D) anxiety/somatization factor score of 7 or higher. Remission was significantly less likely and took longer to occur in these patients. 65 Overall, patients with depression and anxiety are likely have more chronic and recurrent forms of illness. 66 67 Unrecognized comorbid depression and anxiety has also been found to be associated with an increased rate of both psychiatric hospital admission and suicide attempts. 68 69

Identifying patients with comorbid anxiety can be challenging for primary care providers, particularly as patients often present with somatic complaints rather than classic psychiatric symptoms. Although a review of the evidence supporting the diagnosis and treatment of anxiety is beyond the scope of this series, we recommend using a screening instrument such as the Generalized Anxiety Disorder (GAD) scale, which has been validated for use in the primary care setting for patients presenting with anxiety or anxiety related symptoms. Findings suggest that the GAD-2, as well as the GAD-7, has an excellent negative predictive value, although only one half of patients with a positive screen actually have generalized anxiety disorder or panic disorder. 70 The primary care provider must therefore follow up with a more in-depth diagnostic interview to confirm any diagnosis.

While SUD and major depressive disorder are distinct clinical entities, they are often seen together, with a high prevalence of comorbidity ranging from 8.6% to 25%. 71 A 2009 meta-analysis of 74 studies of depression and substance use among people with alcohol use disorders found a positive association of depression with concurrent alcohol use and impairment. Although the effect sizes were small, 60.5% of patients with above average levels of depressive symptoms showed above average levels of current alcohol use and impairment, compared with 39.5% of those with below average levels of depressive symptoms. 72

In the STAR*D trial, the demographics and clinical features were compared between those with and without concurrent SUD in 2541 outpatients with depression. Compared with those without SUD, patients with concurrent SUD were more likely to be younger, male, divorced or never married, and at greater current suicide risk and to have an earlier age of onset of depression, greater depressive symptomatology, more previous suicide attempts, more frequent concurrent anxiety disorders, and greater functional impairment (P=0.048 to <0.001). 73 Potential SUD should be identified by asking a few screening questions that can be easily integrated into the primary care consultation. Although a comprehensive discussion of SUD in primary care is beyond the scope of this review, the SBIRT (screening, brief intervention, referral, and treatment) model, an evidence based approach to identifying, reducing, and preventing substance misuse, can be effectively implemented in the primary care setting. 74

Alternate physical diagnoses

Medical conditions that mimic depression should also be excluded at initial presentation. As no single test, examination, or procedure to easily and effectively differentiate the cause of the presenting symptomatology exists, this can be challenging. 75 Four guiding questions have been proposed to aid in establishing a cause and effect relation between psychiatric symptoms and physical findings 76 :

Is the presentation of the psychiatric symptom atypical?

Is the medical condition or substance use temporally related to the psychiatric symptom?

Are the psychiatric symptoms better explained by a primary psychiatric disorder?

Is the psychiatric presentation a direct consequence of a medical illness or substance use?

As part of a comprehensive assessment, the primary care provider should also evaluate onset and duration of symptoms, changes in drug treatment patterns, and any changes in the patient’s baseline psychosocial functionality. The diagnostic approach to ruling out medical conditions mimicking depression requires critical thinking and clinical diligence. 77

Although the aim of this review is not to fully explore the differential diagnosis of all possible physical diagnoses, a few of those commonly encountered in the primary care setting are noted here. Neurological disorders such as Parkinson’s disease, multiple sclerosis, and dementia, in particular, often have symptoms that overlap with those of major depression. 78 Patients with cognitive impairment may present with low mood, and those with major depressive disorder may have poor concentration with impaired executive functioning, 79 making the clinical picture complex. Further assessment with a neurological examination and cognitive testing should be guided by clinical suspicion. Several of the somatic symptoms of depression, such as fatigue or weight loss, also have the potential to be confused with common medical conditions seen in the primary care setting, such as thyroid disorder or anemia. The clinical interview and physical examination should guide any further diagnostic investigation, including laboratory or other diagnostic studies.

Depression and suicide risk

Once the diagnosis of depression has been made, primary care providers should assess patients for risk of suicide. Depression is a major risk factor for both attempted and completed suicide, 80 and a history of self harm attempts, in combination with a history of well developed suicide plans, place the patient at a greater eventual risk of completing a suicide attempt. 81 Almost half of adults who complete suicide have had contact with primary care services in the month before death 82 ; so primary care providers have a critical role to play in suicide prevention.

A 2017 systematic review of 21 studies assessed the risk of bias and diagnostic accuracy for suicide and suicide attempt. A meta-analysis of five instruments across the studies showed that none of the instruments reached the predetermined benchmarks (80% sensitivity and 50% specificity) for the suicide outcome or suicide attempt outcome. With few exceptions, low figures were observed for the positive predictive value for the suicide outcome (1-13%). 83 This review, however, did not include the diagnostic accuracy of the Suicide Assessment Scale (SUAS) or the Columbia Suicide Severity Rating Scale (C-SSRS). The latter has a strong psychometric evidence base, 84 and it has been validated for use in the primary care setting with color coded risk stratification for easy triage. The decision to intervene, however, must ultimately be based on the primary care provider’s clinical assessment of risk. 85 In questioning a patient perceived to be at risk, primary care providers should specifically ask about suicide with a focus on suicidal thoughts, plans for suicide, and intent. They should assess the level of risk and define the level of care needed. If any uncertainty exists about either of these levels, consultation with a psychiatrist should be considered. 86

Emerging diagnostic approaches

The use of validated screening and assessment rating tools and diagnostic criteria to complement the routine clinical interview is the current expected standard for primary care providers to identify and diagnose depression and track response to treatment. Attempts to standardize measurement based practice have gained significant support, although variability and inaccuracies still exist. Researchers are exploring new technologies that could provide more accurate diagnosis of depression, including genomic, proteomic, and metabolic profiling to identify biomarkers for major depressive disorder. 87 Although the applicability of such biological techniques in the primary care setting remains to be seen, other groups have been developing tools for collecting and analyzing passively collected behavioral and cognitive data (for example, through smartphones) as a form of “digital phenotyping.” 88

Primary care providers face many challenges as they respond to the growing health needs and psychosocial complexity of their patients. Increasingly, they play a central role in the screening, diagnosis, and treatment of depression. Depression in primary care thus must be managed like any other chronic disease. 89 Table 1 shows our recommendations. Systems based interventions must enable primary care practices to both screen and accurately diagnose depression in a setting where effective treatment and appropriate follow-up are available.

Summary of recommendations

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Questions for future research

As a research community, how can we unravel the biological and behavioral heterogeneity of depression to improve the diagnosis and treatment of depression in primary care?

What primary prevention interventions are effective in the primary care setting to reduce the incidence of depression?

What is the most effective approach to stratifying the risk of suicide in patients who are depressed and present with suicidal ideation in the primary care setting?

What can be done as a society to reduce the stigma of depression and improve the perceptions of people with depression within the general population?

Patient involvement

We sought feedback from several different sources in compiling and modifying our reviews. Members of the Wellbeing Network Hounslow Community who live with depression in west London gave us feedback on the written manuscripts. Members of the Ealing Primary Care Mental Health Services Service Users Forum also shared insights from their care being transferred from specialist to primary care services in England. Finally, we sought individual and personal insight from a retired GP who has lived with paraplegia from a spinal cord injury for many years as well as with major depressive disorder, which is treatment resistant. The depression preceded the spinal cord injury and is unrelated to it. Their feedback is provided in detail in our second review. 91

• Clinical review, doi: 10.1136/bmj.l835

Series explanation: State of the Art Reviews are commissioned on the basis of their relevance to academics and specialists in the US and internationally. For this reason they are written predominantly by US authors

Contributors: All three authors made substantial contributions to the conception or design of the work and the acquisition, analysis, or interpretation of data for the work and to drafting the work or revising it critically for important intellectual content; had final approval of the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Competing interests: We have read and understood the BMJ policy on declaration of interests and declare the following interests: none.

Provenance and peer review: Commissioned; externally peer reviewed.

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Open Access

Peer-reviewed

Research Article

Case management interventions in chronic disease reduce anxiety and depressive symptoms: A systematic review and meta-analysis

Roles Conceptualization, Methodology, Visualization, Writing – original draft

Affiliation Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Center of Expertise for Parkinson & Movement Disorders, Radboud University Medical Center, Nijmegen, The Netherlands

Roles Methodology, Writing – original draft

Roles Writing – original draft, Writing – review & editing

Roles Supervision, Writing – review & editing

Roles Writing – review & editing

Affiliation Scientific Center for Quality of Healthcare, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands

Roles Conceptualization, Methodology, Writing – review & editing

* E-mail: [email protected]

• Angelika D. Geerlings, 
• Jules M. Janssen Daalen, 
• Jan H. L. Ypinga, 
• Bastiaan R. Bloem, 
• Marjan J. Meinders, 
• Marten Munneke, 
• Sirwan K. L. Darweesh

• Published: April 14, 2023
• https://doi.org/10.1371/journal.pone.0282590
• Peer Review
• Reader Comments

There is no systematic insight into the effect of case management on common complications of chronic diseases, including depressive symptoms and symptoms of anxiety. This is a significant knowledge gap, given that people with a chronic disease such as Parkinson Disease or Alzheimer’s Disease have identified care coordination as one of their highest priorities. Furthermore, it remains unclear whether the putative beneficial effects of case management would vary by crucial patient characteristics, such as their age, gender, or disease characteristics. Such insights would shift from “one size fits all” healthcare resource allocation to personalized medicine.

We systematically examined the effectiveness of case management interventions on two common complications associated PD and other chronic health conditions: Depressive symptoms and symptoms of anxiety.

We identified studies published until November 2022 from PubMed and Embase databases using predefined inclusion criteria. For each study, data were extracted independently by two researchers. First, descriptive and qualitative analyses of all included studies were performed, followed by random-effects meta-analyses to assess the impact of case management interventions on anxiety and depressive symptoms. Second, meta-regression was performed to analyze potential modifying effects of demographic characteristics, disease characteristics and case management components.

23 randomized controlled trials and four non-randomized studies reported data on the effect of case management on symptoms of anxiety (8 studies) or depressive symptoms (26 studies). Across meta-analyses, we observed a statistically significant effect of case management on reducing symptoms of anxiety (Standardized Mean Difference [SMD] = - 0.47; 95% confidence interval [CI]: -0.69, -0.32) and depressive symptoms (SMD = - 0.48; CI: -0.71, -0.25). We found large heterogeneity in effect estimates across studies, but this was not explained by patient population or intervention characteristics.

Conclusions

Among people with chronic health conditions, case management has beneficial effects on symptoms of depressive symptoms and symptoms of anxiety. Currently, research on case management interventions are rare. Future studies should assess the utility of case management for potentially preventative and common complications, focusing on the optimal content, frequency, and intensity of case management.

Citation: Geerlings AD, Janssen Daalen JM, Ypinga JHL, Bloem BR, Meinders MJ, Munneke M, et al. (2023) Case management interventions in chronic disease reduce anxiety and depressive symptoms: A systematic review and meta-analysis. PLoS ONE 18(4): e0282590. https://doi.org/10.1371/journal.pone.0282590

Editor: Vincenzo De Luca, University of Toronto, CANADA

Received: February 23, 2022; Accepted: February 18, 2023; Published: April 14, 2023

Copyright: © 2023 Geerlings et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exists.

1. Introduction

The increasing prevalence of chronic disease poses a substantial burden to the capacity of health care services to provide adequate care the treatment requires continuous monitoring and ongoing interdisciplinary collaboration between health care providers of various disciplines, who ideally deliver proactive care [ 1 ]. However, current health care systems are typically designed to treat chronic diseases using a “one size fits all” approach instead of tailoring care to each patient’s individual needs [ 2 , 3 ]. Consequently, persons with chronic disease often become responsible for coordinating their care. As a result, patients receive fragmented and ineffective care in achieving the desired health outcomes [ 1 , 3 ]. This increases health care costs and causes an unnecessary burden on patients and their carers, which, in turn, negatively affects their quality of life.

To address these challenges, case management has been introduced to improve the care coordination [ 4 , 5 ]. Field defines case management in different ways [ 4 , 6 ]. However, the everyday basis for each definition is that case management is a collaborative process involving one case manager or a small team that plans, coordinates and reviews the delivery of health care services to meet a patient’s individual needs [ 7 ]. According to this integrated care approach, a case manager takes over the responsibility of managing non-acute services. The interpretation of case management can vary substantially. Still, common core elements include the development and review of individualized care plans, organization of multidisciplinary case meetings, screening and monitoring of risk factors and symptoms, use of evidence-based guidelines, information support for involved physicians, empowerment of patients through providing education, and enhancing self-management skills [ 7 – 9 ].

There is emerging evidence for the promising effect of case management interventions on reducing hospital (re-)admissions and length of stay in patients with chronic diseases such as asthma [ 10 , 11 ], diabetes [ 12 , 13 ], chronic heart failure [ 13 , 14 ] or established obstructive pulmonary diseases [ 15 , 16 ]. However, it is unknown whether case management benefits the most common complications in chronic diseases, predominantly depressive symptoms or symptoms of anxiety. In about 40–50% of people with PD, clinically significant depressive symptoms occur [ 17 ]. Symptoms of anxiety is also very common among people with PD, with an average prevalence of 31% [ 18 ]. Anxiety and depressive symptoms can contribute to more severe motor and cognitive symptoms, and have a negative impact on the perceived quality of life of people with PD as well as their caregivers [ 19 , 20 ]. These complications can occur alongside other manifest symptoms or signs of the disease and, in case of PD, might even precede the onset of motor symptoms. Some complications can also occur as a secondary consequence of the original disease itself (for example, a reactive depression) and worsen disease outcomes, creating a vicious cycle. Therefore, this is an important knowledge gap, especially given that people living with a chronic disease such as PD or Alzheimer’s disease have identified care coordination as one of their highest priorities. Furthermore, it remains unclear whether the putative beneficial effects of case management on common complications would vary by crucial patient characteristics, such as demographics (i.e., age or gender), disease severity or duration, or critical elements of the intervention (e.g., the number of individual patient contact). Such insights would facilitate a more comprehensive deployment of case management to susceptible subgroups of patients, thereby contributing to a broader shift from “one size fits all”-based healthcare resource allocation to “personalized” medicine.

To close these gaps in knowledge, we conducted a systematic review and meta-analysis to examine: 1) the extent and quality of evidence for the effectiveness of case management interventions on common using insights from various chronic diseases; and 2) to what extent putative effects of case management vary across patient subgroups.

This systematic review is guided according to the PRISMA checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) [ 21 ].

2.1. Search focus

Initially the study was set up to study the effectiveness of case management interventions on common and preventable complications in PD, focusing on the following common potentially preventable complications associated with PD [ 22 , 23 ]: (1) depressive symptoms and symptoms of anxiety; (2) fractures or injuries caused by falls; (3) swallowing impairment; (4) urinary tract infections; and (5) neuro-psychiatric disorders, including hallucinations. However, an initial search that focused exclusively on case management interventions in people with PD yielded very few results. We, therefore, expanded the scope of this review by including data on case management interventions in people with other chronic health conditions, in whom we hypothesized case management interventions would have similar effects as in people with PD. Although symptoms and prognosis of PD are considerably different from other chronic disease, we believed that there is a substantial overlap in the composition of case management interventions across different chronic disease and that several components are transferrable, such as medication review, a case manager as main contact person and the use of individualized care plans. Specifically, we broadened the search by including all chronic diseases in which case management has been investigated: Alzheimer’s disease, asthma, cancer, chronic obstructive pulmonary disease (COPD), chronic heart failure, dementia, diabetes, hypertension, multiple sclerosis, and rheumatoid arthritis.

2.2. Search strategy

An initial and limited search for empirical literature was undertaken by one reviewer [ADG] in May 2019 in PubMed to identify important Medical Subject Headings (MeSH) and keywords describing relevant articles. Next, a systematic search for research-based literature was performed by two independent reviewers [ADG, JMJD] using the identified MeSH terms and keywords. We exclusively focused on published articles referenced in PubMed and Embase online in July 2019. The following MeSH terms were used to identify studies on case management: “Case Management,” “Disease Management,” “Patient Care Management,” “Patient Care Planning,” and “Patient-Centered Care,” which were combined with relevant keywords. The detailed search strategy for PubMed can be found in Supplemental Data I in S2 File . Finally, the reference lists of included studies were screened to identify studies missed by the search. A verification search was performed in November 2022.

2.3. Selection criteria

Abstracts and titles of all obtained studies were independently and systematically examined for the selection criteria by two reviewers [ADG, JMJD], and disagreements were resolved during consensus meetings with a third reviewer [SKLD]. Search limits were applied to include only English articles and those published in a peer-review journal. We included in this systematic review studies that (1) used an observational (prospective and retrospective) or interventional study design; (2) included results on the association of a case management intervention with at least one of the five common and potentially preventable complications; (3) included populations who were diagnosed with one of the selected chronic diseases [aged ≥ 18 years]; and (4) described the case management intervention clearly and contained at least three core elements; (5) defined a clear control group, usually receiving usual care; and (6) reported at the minor measures of the distribution of age and gender for the intervention and control group, as potential confounders ( Table 1 ). We excluded studies involving participants living in residential nursing homes, as those participants received 24-hour care and were thus not comparable to other populations receiving case management. Also excluded were studies that described only study protocols or conference abstracts.

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2.4. Data extraction and collection

The quality of the articles was evaluated by two authors (JMJD and ADG) using the Cochrane risk of bias tool for randomized controlled trials (RCTs) [ 24 ] and the ROBINS-I tool for nonrandomized studies [ 25 ]. Two review authors [ADG, JMJD] independently screened and evaluated the studies. The following data were extracted from each study: author, publication year, trial design, country, the aim of the study, study design, distribution of participant characteristics (sex, age, and diagnosis of disease), aspects of case management intervention ( Table 1 ), parts of care received by the control group, follow-up duration, study outcomes, and primary outcome results (mean, confidence intervals, standard deviation, standard error, interquartile range). Standard deviations (SD) were calculated from standard errors (SE). If no SD or SE was reported, authors were contacted and asked to provide additional data. We did not receive further data [ 23 – 27 ] for five articles, so these articles could not be included in the meta-analysis and random-effects regression analysis.

2.5. Statistical analysis

We calculated standardized effect sizes for the main results of each study. Subsequently, we conducted meta-analyses using restricted maximum likelihood (REML) and DerSimonian-Laird estimator in a random-effects model, because of significant heterogeneity between estimates across studies as reflected in the I2 statistic. We used funnel plot visualization and Egger’s test for funnel plot asymmetry to assess whether there was evidence for small study effects.

In addition, predefined random-effects regression analyses were performed to identify potential effect modifiers if a sufficient number of studies (> 3) was available within a complication category (e.g., depressive symptoms, symptoms of anxiety). We included the following study population characteristics as potential effect modifiers: mean age, percentage of female participants, and disease group (neurodegenerative disease versus other chronic disease). Mean age and percentage of females were treated as numeric variables, whereas disease group was recoded into a binary variable with 0 for “neurodegenerative disease” and 1 for “other chronic disease”. We also included the number of case management components (out of nine defined components, which are outlined in Table 1 ) as a potential effect modifier. Also this variable was recoded into a binary variable with 0 for “was not described as part of the case management intervention” and 1 for “was described as part of the case management intervention”. Due to restrictions in the number of potential effect modifiers, we could not include individual components in the random-effects regression analyses for symptoms of anxiety. Instead, we had the number of included case management components as potential effect modifiers. Two studies were excluded from this analysis on depressive symptoms, as the necessary data for regression analysis was not available [ 26 , 27 ].

Multicollinearity of covariables–the modifying effect of covariables on each other—was assessed using variance inflating factors (VIF). Of note, analyses for symptoms of anxiety and depressive symptoms were conducted by using standardized mean difference as outcome and used the same meta-analysis settings. However, because of statistical constraints due to less studies on symptoms of anxiety which bears the risk to overfit the meta-regression model as the number of studies per examined covariate is low, we had to limit the number of covariables in our meta-regression analysis for symptoms of anxiety. Therefore, we added mean age, sex and number of case management components as covariables for the meta-regression analysis for symptoms of anxiety. For the meta-regression analysis for depressive symptoms, by contrast, we added the following covariables: mean age, sex, female, duration of follow-up (less than 6 months, 6 to 12 months, and more than 12 months), disease group (neurodegenerative disease versus other chronic disease) and the individual components of case management interventions (development and review of individualized care plans, in-person contact with the case manager, medication review, provision of education on disease management and treatment, self-management support, support and training for healthcare providers, therapy adherence, and use of evidence-based guidelines).

We considered associations with p<0.05 to be statistically significant across meta-analyses and meta-regression analyses. Analyses were conducted in R [ 28 ], using packages metafor [ 29 ] and ggplot2 for visualization [ 30 ].

3.1. Study selection

The combined PubMed and Embase searches yielded 4765 unique records. After abstract review, 57 full-text articles were assessed for eligibility, of which 23 fulfilled our selection criteria. The other 34 were excluded for the following reasons: 25 studies did not provide relevant data on the topic under study; four studies were not identified as case management interventions; two studies dealt with a different health population; two studies did not include original data, and one study presented the same cohort. Two further studies [ 26 , 31 ] were identified through cross-reference checking and two study [ 32 , 33 ] was added through verification search, bringing the total to 27 included studies. Fig 1 provides an overview of the search and study selection process. A verification search was performed in November 2022 yielding two additional studies.

https://doi.org/10.1371/journal.pone.0282590.g001

3.2. Study characteristics

Table 2 describes the characteristics of the included studies. Of the 27 studies included, 23 [ 26 , 27 , 31 , 33 – 52 ] were RCTs and four were non-randomized intervention studies [ 32 , 53 – 55 ]. The studies were published between 2002 and 2020 and evaluated case management interventions in various countries. The majority of studies evaluated case management interventions among patients with a single health condition [ 26 , 27 , 32 , 34 – 39 , 41 – 44 , 46 , 47 , 49 – 55 ], whereas five studies [ 31 , 33 , 40 , 45 , 48 ] included patients with two or more different chronic diseases, such as heart failure and asthma or COPD. Only one of the included studies was conducted among people with PD. Taken together, the studies included 3752 participants ascribed to case management interventions. Mean age ranged from 57 years to 80 years, with an average age across all studies of 65 years, with 51% being female among the 25 studies [ 26 , 31 – 55 ] reporting gender. Three thousand six hundred eighty-two participants were ascribed to the usual care group. The mean age ranged from 51 years to 78 years, with an average age across all studies of 51 years, with 51% being women.

https://doi.org/10.1371/journal.pone.0282590.t002

3.3. Risk of bias and quality assessment

Of the four non-randomized intervention studies, three were rated as carrying a high risk of bias and one as moderate. Of the RCTs, only eight studies [ 26 , 31 , 33 , 34 , 36 , 37 , 40 , 51 ] were judged to be of excellent methodological quality and at low risk of carrying bias. The remaining 15 studies were of low or moderate quality with a high or unknown risk of bias among several domains. However, most RCTs were rated as having severe or unfamiliar trouble on the parts of blinding participants and blinding of outcome assessment, which is less applicable to this kind of intervention. Details on the quality assessment are presented in Supplemental Data II in S2 File .

3.4. Components of case management intervention

Across the 27 included studies, there was substantial heterogeneity across the content and duration of the various case management interventions. Table 3 displays the different strategies used in each study. Typical components of case management among the 27 studies were (1) regular telephone contacts combined with in-person visits; (2) monitoring of signs, symptoms, and risk factors; (3) ensuring therapy adherence; and (4) providing educational support on disease management and treatment or training on self-management skills. However, the content and structure of these components varied highly among these studies. For instance, in-person home visits ranged from developing and discussing a therapeutic plan with the patient in one study [ 41 ] to monitoring changes in signs and symptoms and reviewing patients’ safety in their home environment in another study [ 36 ]. The location of in-person visits also varied from the patient’s home to a clinical setting. Most studies were conducted through a combination of in-person and telephone contacts, with only seven studies [ 32 , 37 , 38 , 43 , 46 , 47 , 54 ] reporting an intervention exclusively conducted through telephone contact.

https://doi.org/10.1371/journal.pone.0282590.t003

More commonly reported case management interventions, assisted with social and financial support, organized multidisciplinary case meetings and medication reviews, and developed individualized care plans. In only four studies, case management interventions also included the support of informal carers [ 33 , 35 , 38 , 42 ]. And of these, only one study had a regular assessment of the carer’s physical health and education on the carer’s coping skills and educating carers in the disease management [ 35 ].

Furthermore, several studies incorporated technological support systems, which supported the implementation of case management strategies and offered new possibilities. For instance, a web-based service facilitates communication between the patient and the care team, schedules patient contacts, and keeps track of progress and current disease treatment [ 35 ]. In a different study, a web-based collaborative intervention facilitated peer-to-peer support for patients with cancer through a chat room connecting all enrolled participants [ 27 ].

Of the 27 studies, 23 [ 27 , 31 – 33 , 35 – 46 , 49 – 55 ] reported that case management interventions were delivered by a nurse case manager or a team consisting of a nurse case manager and other health care specialists. In one study, [ 26 ], the case manager was a depression clinical specialist without further clarification of the specialist’s background. In another one, [ 34 ], two research coordinators fulfilled the role of care managers. In the other two studies, the background of the case manager was not specified any further by [ 47 , 48 ]. The length of follow-up ranged from one and a half months to 24 months, with 18 studies reporting a 12-month or even longer follow-up period ( Table 2 ).

3.5. Overview of outcome measurements

Results of the narrative data synthesis are summarized and presented in Table 2 . Nearly all included studies evaluated the effectiveness of case management interventions on depressive symptoms, whereas symptoms of anxiety was addressed in only eight studies [ 38 , 41 , 44 , 47 – 50 , 53 ]. None of the included studies reported falls, urinary tract infections, swallowing impairment, or hallucinations. Of note, we included individuals with depressive and anxiety symptoms, not necessarily reflecting individuals with a DSM-5 diagnosis such as major depressive disorder (MDD).

3.5.1. Effect of case management on symptoms of anxiety.

Eight studies (n = 1239 participants) reported outcomes on symptoms of anxiety [ 38 , 41 , 44 , 47 – 50 , 53 ]. The most commonly used scale was the Hospital Anxiety and Depression Scale (HADS) [ 38 , 41 , 44 , 47 , 49 , 50 ], followed by 7-item Generalized Anxiety Disorder [ 48 ], and State Trait Anxiety Index (STAI) [ 53 ]. Six studies reported sufficient data and were included in a random-effects meta-analysis, the results of which revealed a significant effect of case management interventions in decreasing symptoms of anxiety (Standardized Mean Difference [SMD] = - 0.47; 95% confidence interval [CI]: -0.69, -0.324 with moderate heterogeneity (I 2 = 51.9%) ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0282590.g002

Variance inflating factor (VIF) analysis showed no evidence of multicollinearity (all VIFs <5). A tendency for lower SMD was found for studies with a higher percentage of females [standardized regression coefficient β = 0.01, p value = 0.05]. No significant effect was found for mean age of intervention group [β = 0.02, p = 0.13] and number of components were [β = 0.13, p = 0.21].

3.5.2. Effect of case management on depressive symptoms.

With the exception of one study [ 53 ], all studies (n = 7314 participants) reported depressive symptoms measurements using a total of six depression scales, including the 9-item Patient Health Questionnaire (PHQ-9) [ 26 , 34 , 36 , 37 , 40 , 42 , 45 , 46 , 48 , 52 , 54 , 55 ], the Hospital Anxiety/Depression Scale (HADS) [ 38 , 41 , 44 , 47 , 49 , 50 ], the Center for Epidemiologic Studies Depression scale (CES-D) [ 27 , 33 , 39 , 40 ], 20-item Symptom Checklist (SCL-20) [ 31 , 43 , 50 , 52 ], the Cornell Scale for depression [ 35 ] and Taiwanese Depression Questionnaire [ 32 ].

Twenty studies reported sufficient data and were included in the meta-analysis ( Fig 3 ). Two studies [ 40 , 52 ] reported two different depression outcomes (CES-D/SCL-20 PHQ-9), but for analytical reasons, only one (PHQ-9) was included in the meta-analysis. A random-effects meta-analyses revealed a significant effect of case management intervention on depressive symptoms (SMD = - 0.48; CI: -0.71, -0.25), but heterogeneity was high (I 2 = 92.3%). A funnel plot and Egger’s test of funnel plot asymmetry (p = 0.57) showed no evidence for publication bias.

https://doi.org/10.1371/journal.pone.0282590.g003

Variance inflating factor (VIF) analysis showed no evidence of multicollinearity (all VIFs<5). There were no significant population or disease characteristics: neurodegenerative diseases (reference: other chronic disease) [β = 0.20, p = 0.80], mean age of intervention group [β = -0.01, p = 0.76] and percentage of females assigned to case management intervention [β = -0.01, p = 0.43]. Moreover, no significant effect was found for any of the individual case management components: development and review of individualized care plans [β = -0.60, p = 0.21]; in-person contact with the case manager [β = -0.09, p = 0.89]; medication review [β = 0.39, p = 0.47]; provision of education on disease management and treatment [β = -0.08, p = 0.91]; self-management support [β = -0.24, p = 0.70]; support and training for healthcare providers [β = 0.05, p = 0.92]; therapy adherence [β = 0.73, p = 0.92] and use of evidence-based guidelines [β = 0.27, p = 0.52].

4. Discussion

This systematic review and meta-analysis show that case management is more effective than usual care at reducing depressive symptoms and symptoms of anxiety, two common preventable complications associated with many chronic diseases, including PD. This effect persisted for less complex case management interventions (whereby complexity was based on the number of included elements), across countries, for different chronic diseases and given the nearly equal representation of female and male participants across both genders. Contrary to our hypotheses, we found no evidence for effect modification by case management intervention, study population characteristics, or duration of follow-up. Our data showed that the beneficial effects of case management interventions on symptoms of anxiety were somewhat more distinct in studies with a higher percentage of men and studies with a relatively old population. However, both observations were not statistically significant. No similar trends were observed for the effects of case management interventions on depressive symptoms. We did not identify any studies reporting on falls, hallucinations, swallowing impairment, and urinary tract infections, so the effect of case management on those complications remains unclear. Although initially we specifically looked at common complications in PD, only one of the included interventions was conducted in people with PD [ 37 ], indicating that case management interventions in PD are still rare. Although we specifically looked at common complications in PD in the first place, only one of the included interventions was actually conducted in people with PD [ 37 ], indicating that research on the effectiveness of case management interventions in PD is still rare. We want to emphasize that to our knowledge case management interventions are fairly common in clinical practice, but research with well-designed RCT’s or even cohort studies on the topic of case management are rare. The randomized study reported that a PD nurse-led care management intervention among veterans led to better adherence to quality-of-care indicators. The screening instrument showed significant improvement among the intervention group compared to usual care [ 37 ]. In this trial, the PD nurse used four strategies to specify the PD problems of each veteran and to develop an action plan: (1) a telephone-administered assessment to identify 28 problem areas; (2) evidence-based care protocols and, if not available, use of expert consensus on care management; (3) patient portal for communication purposes; and (4) documentation templates to provide care that is patient-centered and coordinated. However, this study is limited by geographical and time factors.

Our study extends the findings of previous studies indicating a beneficial effect of case management on patients’ clinical health outcomes and functioning in everyday life [ 5 , 56 , 57 ]. While previous research on case management has focused on its effects on reducing hospital (re-)admissions, length of stay, and costs, little research has been done regarding its potential to reduce disease complications. Our systematic review addresses this gap in knowledge in a disease in which case management will likely have high potential. The findings of this systematic review favor case management interventions over usual care and suggest that even less complex case management interventions have a beneficial effect on symptoms of anxiety and depression, which have an immense impact on the quality of life. Our meta-analysis suggests a modest effect size of case management interventions in reducing feelings symptoms of anxiety and depressive symptoms. However, this finding needs to be interpreted with caution as the magnitude of reduction in symptoms of anxiety and depression scores does not automatically translate into a lower risk of anxiety and depressive disorder. Large-scale studies assessing the personal impact of case management on clinically relevant measures of anxiety and depressive symptoms are therefore warranted. Moreover, several indirect working mechanisms may have contributed to the beneficial effect on depressive symptoms and stress ( Fig 4 ), which can immensely impact on the quality of life and even mortality in patients with PD [ 58 – 60 ]. First, patients across studies received personalized management of their chronic health conditions, including individually tailored health information and problem-solving strategies provided by a case manager. Second, the availability of one main contact person for new issues and establishing a personal relationship between patients and case managers may have helped reduce symptoms of anxiety and depressive symptoms. Notably, previous research on improving PD care revealed that having a single point of access was rated as the top priority by people with PD [ 2 ]. Third, having little information about the rate of disease progression and treatment options is known to enhance symptoms of anxiety in patients. Patient education eventually allows for more shared decision-making and, thereby, a treatment better tailored to a patient’s individual needs and coping behavior, which, in turn, might alleviate symptoms of anxiety.

https://doi.org/10.1371/journal.pone.0282590.g004

Several methodological considerations need to be considered. First, heterogeneity across studies was high, and effect estimates varied substantially between studies, which might have affected the results of our meta-regression, leading to the inability to identify any covariables of our effect estimates. Furthermore, since only two studies [ 39 , 49 ] followed patients for longer than two years, the long-term effectiveness of case management interventions still needs to be determined. Second, only a few included studies were of good methodological quality. In particular, limited blinding of outcome assessment, insufficient details on the specification of the efficacy of specific case management elements, and the lack of participant selection limited our ability to assess case management impact more accurately. In addition, as a systematic search strategy cannot screen full-text articles, the risk remains that the used search strategy needs to capture relevant articles. Eggers et al. [ 61 ], for instance, conducted an RCT with a nurse case-manager-led intervention with patients with PD and reported on the effectiveness of CM in reducing hospitalization caused by falls. As these findings have yet to be reported in the study’s abstract, this study was not captured through our search strategy. Furthermore, the use of antidepressant or anti-anxiety medication may have affected the results. Still, since some of the original studies included in this meta-analysis did not report sufficient information on medication use, we could not assess this in the meta-regression analysis. For example, an intensive and proactive medication review schedule as part of a case management intervention might have contributed to better outcomes in the intervention group. Finally, although case management interventions as part of research trials are generally free of cost and well-accessible for recruitment purposes, the merits of case management interventions in daily life might be influenced by cost constraints and limited access to care. Specifically, this would hamper structured clinical take-up in private healthcare or increase the social disparity.

Our data showed that the beneficial effects of case management interventions on symptoms of anxiety were somewhat more distinct in studies with a higher percentage of men and studies with a relatively old population. However, both observations were not statistically significant. No similar trends were observed for the effects of case management interventions on depressive symptoms. This systematic review identified the need for further research into most compelling case management interventions and components, the optimal intensity and frequency of the individual case management strategies, and their interaction with patient characteristics. Once this knowledge becomes available, case management interventions can be implemented that are better tailored to individual needs and, as such, presumably more effective. Moreover, this systematic review revealed that case management implementation is more common among certain chronic diseases than others; the commonest ones were diabetes and heart failure, while only one included study concerned patients with PD. It should be noted that anxiety and depression in PD can be both comorbid and arise due to disease burden. It is unknown whether these different causes should be treated differently and whether the efficacy of case management interventions differs for various reasons or disease groups. As we do not have sufficient data to evaluate this, future case management trials in PD are warranted. In addition, future research is warranted to improve the current evidence base for case management effects on symptoms of anxiety and depressive symptoms in persons with a chronic disease, specifically in persons with PD.

Supporting information

S1 checklist. prisma 2009 checklist..

https://doi.org/10.1371/journal.pone.0282590.s001

https://doi.org/10.1371/journal.pone.0282590.s002

https://doi.org/10.1371/journal.pone.0282590.s003

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• Continuing Education Activity

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. The American Psychiatric Association’s Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) classifies the depressive disorders into Disruptive mood dysregulation disorder; Major depressive disorder; Persistent depressive disorder (dysthymia); Premenstrual dysphoric disorder; and Depressive disorder due to another medical condition. The common features of all the depressive disorders are sadness, emptiness, or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individual’s capacity to function. This activity reviews the evaluation and management of depression and the role of interprofessional team members in collaborating to provide well-coordinated care and enhance patient outcomes.

• Review the risk factors for depression.
• Describe DSM V criteria for the diagnosis of depression.
• Summarize the treatment of depression.
• Outline the evaluation and management of depression and the role of interprofessional team members in collaborating to provide well-coordinated care and enhance patient outcomes.
• Introduction

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. [1] [2] The American Psychiatric Association’s Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) classifies the depressive disorders into:

• Disruptive mood dysregulation disorder
• Major depressive disorder
• Persistent depressive disorder (dysthymia)
• Premenstrual dysphoric disorder
• Depressive disorder due to another medical condition

The common features of all the depressive disorders are sadness, emptiness, or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individual’s capacity to function. [3]

Because of false perceptions, nearly 60% of people with depression do not seek medical help. Many feel that the stigma of a mental health disorder is not acceptable in society and may hinder both personal and professional life. There is good evidence indicating that most antidepressants do work but the individual response to treatment may vary. 

The etiology of major depressive disorder is multifactorial with both genetic and environmental factors playing a role. First-degree relatives of depressed individuals are about 3 times as likely to develop depression as the general population; however, depression can occur in people without family histories of depression. [4] [5]

Some evidence suggests that genetic factors play a lesser role in late-onset depression than in early-onset depression. There are potential biological risk factors that have been identified for depression in the elderly. Neurodegenerative diseases (especially Alzheimer disease and Parkinson disease), stroke, multiple sclerosis, seizure disorders, cancer, macular degeneration, and chronic pain have been associated with higher rates of depression. Life events and hassles operate as triggers for the development of depression. Traumatic events such as the death or loss of a loved one, lack or reduced social support, caregiver burden, financial problems, interpersonal difficulties, and conflicts are examples of stressors that can trigger depression.

• Epidemiology

Twelve-month prevalence of major depressive disorder is approximately 7%, with marked differences by age group.  The prevalence in 18- to 29-year-old individuals is threefold higher than the prevalence in individuals aged 60 years or older. Females experience 1.5- to 3-fold higher rates than males beginning in early adolescence.  In the US, depression affects nearly 17 million adults but these numbers are gross underestimates as many have not even come to medical attention.

• Pathophysiology

The underlying pathophysiology of major depressive disorder has not been clearly defined. Current evidence points to a complex interaction between neurotransmitter availability and receptor regulation and sensitivity underlying the affective symptoms.

Clinical and preclinical trials suggest a disturbance in central nervous system serotonin (5-HT) activity as an important factor. Other neurotransmitters implicated include norepinephrine (NE), dopamine (DA), glutamate, and brain-derived neurotrophic factor (BDNF).

The role of CNS 5-HT activity in the pathophysiology of major depressive disorder is suggested by the therapeutic efficacy of selective serotonin reuptake inhibitors (SSRIs). Research findings imply a role for neuronal receptor regulation, intracellular signaling, and gene expression over time, in addition to enhanced neurotransmitter availability.

Seasonal affective disorder is a form of major depressive disorder that typically arises during the fall and winter and resolves during the spring and summer. Studies suggest that seasonal affective disorder is also mediated by alterations in CNS levels of 5-HT and appears to be triggered by alterations in circadian rhythm and sunlight exposure.

Vascular lesions may contribute to depression by disrupting the neural networks involved in emotion regulation—in particular, frontostriatal pathways that link the dorsolateral prefrontal cortex, orbitofrontal cortex, anterior cingulate, and dorsal cingulate. Other components of limbic circuitry, in particular, the hippocampus and amygdala, have been implicated in depression.

• History and Physical

The investigation into depressive symptoms begins with inquiries of the neurovegetative symptoms which include changes in sleeping patterns, appetite, and energy levels. Positive responses should elicit further questioning focused on evaluating for the presence of the symptoms which are diagnostic of major depression. These are the 9 symptoms listed in the DSM-5 . Five must be present to make the diagnosis (one of the symptoms should be depressed mood or loss of interest or pleasure):

• Sleep disturbance
• Interest/pleasure reduction
• Guilt feelings or thoughts of worthlessness
• Energy changes/fatigue
• Concentration/attention impairment
• Appetite/weight changes
• Psychomotor disturbances
• Suicidal thoughts
• Depressed mood

All patients with depression should be evaluated for suicidal risk. Any suicide risk must be given prompt attention which could include hospitalization or close and frequent monitoring.

Other areas of investigation include:

• Past medical history and family medical history, and current medications
• Social history with a focus on stressors and the use of drugs and alcohol
• History and physical examination to rule out organic causes of depression. Depressive symptoms and their severity are also evaluated with the help of questionnaires such as the Beck's Depression Inventory (BDI), Hamilton Depression Scale (Ham-D), and Zung Self Rating Depression Scale

The diagnosis of depression is based on history and physical findings. No diagnostic laboratory tests are available to diagnose major depressive disorder. Laboratory studies are, however, useful to exclude medical illnesses that may present as major depressive disorder. [6] [7] [8] These laboratory studies might include the following:

• Complete blood cell (CBC) count
• Thyroid-stimulating hormone (TSH)
• Vitamin B-12
• Rapid plasma reagin (RPR)
• Electrolytes, including calcium, phosphate, and magnesium levels
• Blood urea nitrogen (BUN) and creatinine
• Liver function tests (LFTs)
• Blood alcohol level
• Blood and urine toxicology screen
• Arterial blood gas (ABG)
• Dexamethasone suppression test (Cushing disease, but also positive in depression)
• Cosyntropin (ACTH) stimulation test (Addison disease)  
• Computed tomography (CT) scanning or magnetic resonance imaging (MRI) of the brain should be considered if organic brain syndrome or hypopituitarism is included in the differential diagnosis
• Treatment / Management

Medication alone and brief psychotherapy (cognitive-behavioral therapy, interpersonal therapy) alone can relieve depressive symptoms. Combination therapy has also been associated with significantly higher rates of improvement in depressive symptoms; increased quality of life; and better treatment compliance. There is also empirical support for the ability of CBT to prevent relapse. [9] [10]

Electroconvulsive therapy is useful for patients who are not responding well to medications or are suicidal. [11] [1]

Medications

• Selective serotonin reuptake inhibitors (SSRIs)
• Serotonin/norepinephrine reuptake inhibitors (SNRIs)     
• Atypical antidepressants
• Serotonin-Dopamine Activity Modulators (SDAMs)     
• Tricyclic antidepressants (TCAs)
• Monoamine oxidase inhibitors (MAOIs)  
• Selective serotonin reuptake inhibitors (SSRIs): SSRIs have the advantage of ease of dosing and low toxicity in overdose. They are also the first-line medications for late-onset depression.
• SSRIs include: Citalopram, escitalopram, fluoxetine,  fluvoxamine, paroxetine, sertraline, vilazodone, vortioxetine
• Serotonin/norepinephrine reuptake inhibitors (SNRIs): SNRIs, which include venlafaxine, desvenlafaxine, duloxetine, and levomilnacipran can be used as first-line agents, particularly in patients with significant fatigue or pain syndromes associated with the episode of depression. SNRIs also have an important role as second-line agents in patients who have not responded to SSRIs. 
• Atypical antidepressants: Atypical antidepressants include bupropion, mirtazapine, nefazodone, and trazodone. They have all been found to be effective in monotherapy in major depressive disorder and may be used in combination therapy for more difficult to treat depression.
• Serotonin-Dopamine Activity Modulators (SDAMs): SDAMs include brexpiprazole and aripiprazole. SDAMs act as a partial agonist at 5-HT1A and dopamine D2 receptors at similar potency, and as an antagonist at 5-HT2A and noradrenaline alp Brexpiprazole is indicated as adjunctive therapy for major depressive disorder (MDD).
• Tricyclic antidepressants (TCAS): TCAs include the following: Amitriptyline, clomipramine, desipramine, doxepin, imipramine, nortriptyline, protriptyline, trimipramine. TCAs have a long record of efficacy in the treatment of depression. They are used less commonly because of their side-effect profile and their considerable toxicity in overdose.
• Monoamine oxidase inhibitors (MAOIs): MAOIs include isocarboxazid, phenelzine, selegiline, and tranylcypromine. These agents are widely effective in a broad range of affective and anxiety disorders. Because of the risk of hypertensive crisis, patients on these medications must follow a low-tyramine diet. Other adverse effects can include insomnia, anxiety, orthostasis, weight gain, and sexual dysfunction.

Electroconvulsive Therapy (ECT)

ECT is a highly effective treatment for depression. Onset of action may be more rapid than that of drug treatments, with benefit often seen within 1 week of commencing treatment. A course of ECT (usually up to 12 sessions) is the treatment of choice for patients who do not respond to drug therapy, are psychotic, or are suicidal or dangerous to themselves. Thus, the indications for the use of ECT include the following:

• Need for a rapid antidepressant response  Failure of drug therapies   
• History of a good response to ECT     
• Patient preference     
• High risk of suicide
• High risk of medical morbidity and mortality

Although advances in brief anesthesia and neuromuscular paralysis have improved the safety and tolerability of ECT, this modality poses numerous risks, including those associated with general anesthesia, postictal confusion, and, more rarely, short-term memory difficulties .  

Psychotherapy

Cognitive Behavior Therapy and Interpersonal Therapy are evidence-based psychotherapies that have been found to be effective in the treatment of depression.

Cognitive-behavioral therapy (CBT)

CBT is a structured, and didactic form of therapy that focuses on helping individuals identify and modify maladaptive thinking and behavior patterns (16 to 20 sessions). It is based on the premise that patients who are depressed exhibit the “cognitive triad” of depression, which includes a negative view of themselves, the world, and the future. Patients with depression also exhibit cognitive distortions that help to maintain their negative beliefs. CBT for depression typically includes behavioral strategies (i.e., activity scheduling), as well as cognitive restructuring to change negative automatic thoughts and addressing maladaptive schemas.

There is evidence supporting the use of CBT with individuals of all ages. It is also considered being efficacious for the prevention of relapse. It is particularly valuable for elderly patients, who may be more prone to problems or side effects with medications.  

Mindfulness-based cognitive therapy (MBCT) was designed to reduce relapse among individuals who have been successfully treated for an episode of recurrent major depressive disorder. The primary treatment component is mindfulness training. MBCT specifically focuses on ruminative thought processes as being a risk factor for relapse. Research indicates that MBCT is effective in reducing the risk of relapse in patients with recurrent depression, especially in those with the most severe residual symptoms. Interpersonal therapy (IPT)

Interpersonal Therapy (IPT)

Interpersonal therapy (IPT) is a time-limited (typically 16 sessions) treatment for major depressive disorder. IPT draws from attachment theory and emphasize the role of interpersonal relationships, focusing on current interpersonal difficulties. Specific areas of emphasis include grief, interpersonal disputes, role transitions, and interpersonal deficits.

• Differential Diagnosis
• Adjustment disorders
• Chronic Fatigue syndrome
• Dissociative disorders
• Illness anxiety disorders
• Hypoglycemia
• Hypopituitarism
• Schizoaffective disorders
• Schizophrenia
• Somatic symptom disorders

Major depression has very high morbidity and mortality contributing to high rates of suicide. Even though effective drug treatment is available, nearly 50% may not initially respond. Complete remission is not common but at least 40% achieve partial remission in 12 months.

However, relapses are common and many patients require a variety of treatments to control the symptoms. The quality of life of most patients with depression is poor.

Depression accounts for nearly 40,000 cases of suicide each year in the US. The highest rate of suicides is in older men.

• Enhancing Healthcare Team Outcomes

Depression is a very common disorder encountered by the nurse practitioner, primary care provider, psychiatrist, and mental health worker, coordinating as an interprofessional healthcare team. The disorder has extremely high morbidity including the risk of suicide. All healthcare workers should be knowledgeable about this disorder and refer the patient to a psychiatrist if there is a risk of self-harm.

Education plays an important role in the successful treatment of major depressive disorder. This would include the education of the family and the patient. Lack of accurate information and misperceptions of the illness as a personal weakness or failings leads to painful stigmatization and avoidance of the diagnosis by many of those affected. Patients should know the rationale behind the choice of treatment, potential adverse effects, and expected results.

The involvement of the pharmacist in the treatment plan can enhance medication compliance and referral for psychotherapy. The pharmacist can also check that dosing is appropriate, that there are no significant interactions, and counsel on adverse effects. Engaging family members can be a critical component of a treatment plan. Family members are helpful informants, can ensure medication compliance, be a big source of social support and can encourage patients to change behaviors that perpetuate depression (e.g., inactivity).

Patients with moderate to severe depression should also be seen by a social worker or case management nurse to ensure that they have a support system and finances for treatment. If there is a concern, the person managing the case should present the issues to the interprofessional team so that a plan can be developed to get the patient the care they need.  Overall, depression is managed by an interprofessional team dedicated to the management of mental health disorders. Open communication between all the members of the interprofessional team is the key to lowering the morbidity of the disorder. [Leve 5]

The outcomes for patients with depression are guarded. There is no cure and the condition has frequent relapses and remissions, leading to a poor quality of life. [3] [12] [13]

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Disclosure: Suma Chand declares no relevant financial relationships with ineligible companies.

Disclosure: Hasan Arif declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Chand SP, Arif H. Depression. [Updated 2023 Jul 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression

Affiliation.

• 1 School of Nursing.
• PMID: 24943527
• PMCID: PMC4580372

The purposes of this methodological paper are: 1) to describe data mining methods for building a classification model for a chronic disease using a U.S. behavior risk factor data set, and 2) to illustrate application of the methods using a case study of depressive disorder. Methods described include: 1) six steps of data mining to build a disease model using classification techniques, 2) an innovative approach to analyzing high-dimensionality data, and 3) a visualization strategy to communicate with clinicians who are unfamiliar with advanced statistics. Our application of data mining strategies identified childhood experience living with mentally ill and sexual abuse, and limited usual activity as the strongest correlates of depression among hundreds variables. The methods that we applied may be useful to others wishing to build a classification model from complex, large volume datasets for other health conditions.

Publication types

• Research Support, N.I.H., Extramural
• Research Support, Non-U.S. Gov't
• Chronic Disease / classification*
• Chronic Disease / epidemiology*
• Data Mining / methods*
• Depression / classification
• Depression / epidemiology*
• Electronic Health Records / classification*
• Electronic Health Records / statistics & numerical data*
• Health Behavior*
• Information Storage and Retrieval / methods
• Pattern Recognition, Automated / methods
• Risk Assessment / methods

Grants and funding

• R01 HS019853/HS/AHRQ HHS/United States
• R01 HS022961/HS/AHRQ HHS/United States
• P30 NR010677/NR/NINR NIH HHS/United States
• T32 NR007969/NR/NINR NIH HHS/United States
• R01 NR008903/NR/NINR NIH HHS/United States