Exploratory analyses of effect modifiers in the antidepressant treatment of major depression: Individual-participant data meta-analysis of 2803 participants in seven placebo-controlled randomized trials

What predicts response to sertraline for people with depression in primary care? a secondary data analysis of moderators in the PANDA trial

PURPOSE

Antidepressants are a first-line treatment for depression, yet many patients do not respond. There is a need to understand which patients have greater treatment response but there is little research on patient characteristics that moderate the effectiveness of antidepressants. This study examined potential moderators of response to antidepressant treatment.

METHODS

The PANDA trial investigated the clinical effectiveness of sertraline (n = 326) compared with placebo (n = 329) in primary care patients with depressive symptoms. We investigated 11 potential moderators of treatment effect (age, employment, suicidal ideation, marital status, financial difficulty, education, social support, family history of depression, life events, health and past antidepressant use). Using multiple linear regression, we investigated the appropriate interaction term for each of these potential moderators with treatment as allocated.

RESULTS

Family history of depression was the only variable with weak evidence of effect modification (p-value for interaction = 0.048), such that those with no family history of depression may have greater benefit from antidepressant treatment. We found no evidence of effect modification (p-value for interactions≥0.29) by any of the other ten variables.

CONCLUSION

Evidence for treatment moderators was extremely limited, supporting an approach of continuing discuss antidepressant treatment with all patients presenting with moderate to severe depressive symptoms.

Influence of study characteristics, methodological rigour and publication bias on efficacy of pharmacotherapy in obsessive-compulsive disorder: a systematic review and meta-analysis of randomised, placebo-controlled trials

QUESTION

We examined the effect of study characteristics, risk of bias and publication bias on the efficacy of pharmacotherapy in randomised controlled trials (RCTs) for obsessive-compulsive disorder (OCD).

STUDY SELECTION AND ANALYSIS

We conducted a systematic search of double-blinded, placebo-controlled, short-term RCTs with selective serotonergic reuptake inhibitors (SSRIs) or clomipramine. We performed a random-effect meta-analysis using change in the Yale-Brown Obsessive-Compulsive Scale (YBOCS) as the primary outcome. We performed meta-regression for risk of bias, intervention, sponsor status, number of trial arms, use of placebo run-in, dosing, publication year, age, severity, illness duration and gender distribution. Furthermore, we analysed publication bias using a Bayesian selection model.

FINDINGS

We screened 3729 articles and included 21 studies, with 4102 participants. Meta-analysis showed an effect size of -0.59 (Hedges' G, 95% CI -0.73 to -0.46), equalling a 4.2-point reduction in the YBOCS compared with placebo. The most recent trial was performed in 2007 and most trials were at risk of bias. We found an indication for publication bias, and subsequent correction for this bias resulted in a depleted effect size. In our meta-regression, we found that high risk of bias was associated with a larger effect size. Clomipramine was more effective than SSRIs, even after correcting for risk of bias. After correction for multiple testing, other selected predictors were non-significant.

CONCLUSIONS

Our findings reveal superiority of clomipramine over SSRIs, even after adjusting for risk of bias. Effect sizes may be attenuated when considering publication bias and methodological rigour, emphasising the importance of robust studies to guide clinical utility of OCD pharmacotherapy.

PROSPERO REGISTRATION NUMBER

CRD42023394924.

Development of a model to predict antidepressant treatment response for depression among Veterans

BACKGROUND

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

METHODS

A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

RESULTS

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

CONCLUSIONS

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

A Precision Treatment Model for Internet-Delivered Cognitive Behavioral Therapy for Anxiety and Depression Among University Students: A Secondary Analysis of a Randomized Clinical Trial

Importance

Guided internet-delivered cognitive behavioral therapy (i-CBT) is a low-cost way to address high unmet need for anxiety and depression treatment. Scalability could be increased if some patients were helped as much by self-guided i-CBT as guided i-CBT.

Objective

To develop an individualized treatment rule using machine learning methods for guided i-CBT vs self-guided i-CBT based on a rich set of baseline predictors.

Design, Setting, and Participants

This prespecified secondary analysis of an assessor-blinded, multisite randomized clinical trial of guided i-CBT, self-guided i-CBT, and treatment as usual included students in Colombia and Mexico who were seeking treatment for anxiety (defined as a 7-item Generalized Anxiety Disorder [GAD-7] score of ≥10) and/or depression (defined as a 9-item Patient Health Questionnaire [PHQ-9] score of ≥10). Study recruitment was from March 1 to October 26, 2021. Initial data analysis was conducted from May 23 to October 26, 2022.

Interventions

Participants were randomized to a culturally adapted transdiagnostic i-CBT that was guided (n = 445), self-guided (n = 439), or treatment as usual (n = 435).

Main Outcomes and Measures

Remission of anxiety (GAD-7 scores of ≤4) and depression (PHQ-9 scores of ≤4) 3 months after baseline.

Results

The study included 1319 participants (mean [SD] age, 21.4 [3.2] years; 1038 women [78.7%]; 725 participants [55.0%] came from Mexico). A total of 1210 participants (91.7%) had significantly higher mean (SE) probabilities of joint remission of anxiety and depression with guided i-CBT (51.8% [3.0%]) than with self-guided i-CBT (37.8% [3.0%]; P = .003) or treatment as usual (40.0% [2.7%]; P = .001). The remaining 109 participants (8.3%) had low mean (SE) probabilities of joint remission of anxiety and depression across all groups (guided i-CBT: 24.5% [9.1%]; P = .007; self-guided i-CBT: 25.4% [8.8%]; P = .004; treatment as usual: 31.0% [9.4%]; P = .001). All participants with baseline anxiety had nonsignificantly higher mean (SE) probabilities of anxiety remission with guided i-CBT (62.7% [5.9%]) than the other 2 groups (self-guided i-CBT: 50.2% [6.2%]; P = .14; treatment as usual: 53.0% [6.0%]; P = .25). A total of 841 of 1177 participants (71.5%) with baseline depression had significantly higher mean (SE) probabilities of depression remission with guided i-CBT (61.5% [3.6%]) than the other 2 groups (self-guided i-CBT: 44.3% [3.7%]; P = .001; treatment as usual: 41.8% [3.2%]; P < .001). The other 336 participants (28.5%) with baseline depression had nonsignificantly higher mean (SE) probabilities of depression remission with self-guided i-CBT (54.4% [6.0%]) than guided i-CBT (39.8% [5.4%]; P = .07).

Conclusions and Relevance

Guided i-CBT yielded the highest probabilities of remission of anxiety and depression for most participants; however, these differences were nonsignificant for anxiety. Some participants had the highest probabilities of remission of depression with self-guided i-CBT. Information about this variation could be used to optimize allocation of guided and self-guided i-CBT in resource-constrained settings.

Trial Registration

ClinicalTrials.gov Identifier: NCT04780542.

Time-course of the tDCS antidepressant effect: An individual participant data meta-analysis

INTRODUCTION

Prefrontal transcranial direct current stimulation (tDCS) shows promise as an effective treatment for depression. However, factors influencing treatment and the time-course of symptom improvements remain to be elucidated.

METHODS

Individual participant data was collected from ten randomised controlled trials of tDCS in depression. Depressive symptom scores were converted to a common scale, and a linear mixed effects individual growth curve model was fit to the data using k-fold cross-validation to prevent overfitting.

RESULTS

Data from 576 participants were analysed (tDCS: n = 311; sham: n = 265), of which 468 were unipolar and 108 had bipolar disorder. tDCS effect sizes reached a peak at approximately 6 weeks, and continued to diverge from sham up to 10 weeks. Significant predictors associated with worse response included higher baseline depression severity, treatment resistance, and those associated with better response included bipolar disorder and anxiety disorder.

CONCLUSIONS

Our findings suggest that longer treatment courses, lasting at least 6 weeks in duration, may be indicated. Further, our results show that tDCS is effective for depressive symptoms in bipolar disorder. Compared to unipolar depression, participants with bipolar disorder may require additional maintenance sessions to prevent rapid relapse.

Symptoms that remain after depression treatment in patients with coronary heart disease

OBJECTIVE

Symptoms which commonly remain after treatment for major depression increase the risk of relapse and recurrence in medically well patients. The same symptoms predict major adverse cardiac events in observational studies of patients with coronary heart disease (CHD). The purpose of this study was to determine the prevalence and predictors of residual depression symptoms in depressed patients with CHD-.

METHODS

Beck Depression Inventory-II data from two randomized clinical trials and an uncontrolled treatment study of depression in patients with CHD were combined to determine the prevalence and predictors of residual symptoms.

RESULTS

Loss of energy, loss of pleasure, loss of interest, fatigue, and difficulty concentrating were the five most common residual symptoms in all three studies. They are also among the most common residual symptoms in medically well patients who are treated for depression. The severity of pre-treatment anxiety predicted the post-treatment persistence of all these symptoms except for loss of energy.

CONCLUSIONS

The most common post-treatment residual symptoms found in this study of patients with coronary heart disease and comorbid major depression are the same as those that have been reported in previous studies of medically-well depressed patients. This suggests that they may be resistant to standard depression treatments across diverse patient populations. More effective treatments for these symptoms are needed.

Prognostic factors and effect modifiers for personalisation of internet-based cognitive behavioural therapy among university students with subthreshold depression: A secondary analysis of a factorial trial

BACKGROUND

Internet-cognitive behavioural therapy (iCBT) for depression can include multiple components. This study explored depressive symptom improvement prognostic factors (PFs) and effect modifiers (EMs) for five common iCBT components including behavioural activation, cognitive restructuring, problem solving, self-monitoring, and assertion training.

METHODS

We used data from a factorial trial of iCBT for subthreshold depression among Japanese university students (N = 1093). The primary outcome was the change in PHQ-9 scores at 8 weeks from baseline. Interactions between each component and various baseline characteristics were estimated using a mixed-effects model for repeated measures. We calculated multiplicity-adjusted p-values at 5 % false discovery rate using the Benjamini-Hochberg procedure.

RESULTS

After multiplicity adjustment, the baseline PHQ-9 total score emerged as a PF and exercise habits as an EM for self-monitoring (adjusted p-values <0.05). The higher the PHQ-9 total score at baseline (range: 5-14), the greater the decrease after 8 weeks. For each 5-point increase at baseline, the change from baseline to 8 weeks was bigger by 2.8 points. The more frequent the exercise habits (range: 0-2 points), the less effective the self-monitoring component. The difference in PHQ-9 change scores between presence or absence of self-monitoring was smaller by 0.94 points when the participant exercised one level more frequently. Additionally, the study suggested seven out of 36 PFs and 14 out of 160 EMs examined were candidates for future research.

LIMITATIONS

Generalizability is limited to university students with subthreshold depression.

CONCLUSIONS

These results provide some helpful information for the future development of individualized iCBT algorithms for depression.

A Randomized Clinical Trial Comparing Two Treatment Strategies, Evaluating the Meaningfulness of HAM-D Rating Scale in Patients With Major Depressive Disorder

INTRODUCTION

Due to the complexity of symptoms in major depressive disorder (MDD), the majority of depression scales fall short of accurately assessing a patient's progress. When selecting the most appropriate antidepressant treatment in MDD, a multidimensional scale such as the Hamilton Depression Rating scale (HAM-D) may provide clinicians with more information especially when coupled with unidimensional analysis of some key factors such as depressed mood, altered sleep, psychic and somatic anxiety and suicidal ideation etc.

METHODS

HAM-D measurements were carried out in patients with MDD when treated with two different therapeutic interventions. The prespecified primary efficacy variables for the study were changes in score from baseline to the end of the 12 weeks on HAM-D scale (i.e., ≤ 8 or ≥50% response). The study involved three assessment points (baseline, 6 weeks and 12 weeks).

RESULTS

Evaluation of both the absolute HAM-D scores and four factors derived from the HAM-D (depressed mood, sleep, psychic and somatic anxiety and suicidal ideation) revealed that the latter showed a greater promise in gauging the anti-depressant responses.

CONCLUSION

The study confirms the assumption that while both drugs may improve several items on the HAM-D scale, the overall protocol may fall short of addressing the symptoms diversity in MDD and thus the analysis of factor (s) in question might be more relevant and meaningful.

The impact of sleep, physical activity and sedentary behaviour on symptoms of depression and anxiety before and during the COVID-19 pandemic in a sample of South African participants

During lockdowns associated with the COVID-19 pandemic, individuals have experienced poor sleep quality and sleep regularity, changes in lifestyle behaviours, and heightened depression and anxiety. However, the inter-relationship and relative strength of those behaviours on mental health outcomes is still unknown. We collected data between 12 May and 15 June 2020 from 1048 South African adults (age: 32.76 ± 14.43 years; n = 767 female; n = 473 students) using an online questionnaire. Using structural equation modelling, we investigated how insomnia symptoms, sleep regularity, exercise intensity/frequency and sitting/screen-use (sedentary screen-use) interacted to predict depressive and anxiety-related symptoms before and during lockdown. We also controlled for the effects of sex and student status. Irrespective of lockdown, (a) more severe symptoms of insomnia and greater sedentary screen-use predicted greater symptoms of depression and anxiety and (b) the effects of sedentary screen-use on mental health outcomes were mediated by insomnia. The effects of physical activity on mental health outcomes, however, were only significant during lockdown. Low physical activity predicted greater insomnia symptom severity, which in turn predicted increased depressive and anxiety-related symptoms. Overall, relationships between the study variables and mental health outcomes were amplified during lockdown. The findings highlight the importance of maintaining physical activity and reducing sedentary screen-use to promote better sleep and mental health.

Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis

AIMS

To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.

METHODS

Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3-4, 6-8, and 9-12 months post-baseline and remission at 3-4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/.

RESULTS

There was no evidence of an association between age and prognosis before or after adjusting for depressive 'disorder characteristics' that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3-4 months post-baseline per-5-year increase in age = 0(95% CI: -0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3-4 months or 9-12 months post-baseline, but men had worse prognoses at 6-8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6-8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive 'disorder characteristics' and employment status (12.23% (-1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive 'disorder characteristics' and all available confounders.

CONCLUSION

Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive 'disorder characteristics' in clinic may be important.

Physical and social anhedonia are associated with suicidality in major depression, but not in schizophrenia

OBJECTIVE

This cross-sectional study investigated the association of physical and social anhedonia with suicidality in patients with major depressive disorder (MDD), schizophrenia, and in non-psychiatric controls.

METHOD

All participants completed the revised Physical Anhedonia Scale (RPAS) and the revised Social Anhedonia Scale (RSAS) and were subdivided according to positive life-time suicide attempt history. MDD patients were evaluated with the Montgomery-Ãsberg Depression Rating Scale (MADRS), healthy respondents with the Patient Health Questionnaire-9 (PHQ-9), and schizophrenia patients with the Calgary Depression Scale for Schizophrenia (CDSS).

RESULTS

In 683 study participants, the prevalence of each anhedonia was the highest in MDD, followed by schizophrenia, and lowest in the control group. Among MDD patients, those with physical and social anhedonia had greater rates of recent suicidal ideation, while a higher frequency of individuals with life-time suicide attempts was detected in those with only social anhedonia. In contrast, no association between either anhedonia and life-time suicide attempts or recent suicidal ideation was found in patients with schizophrenia.

CONCLUSIONS

Assessing social and physical anhedonia might be important in MDD patients, given its association with both life-time suicide attempts and recent suicidal ideation. Suicidality in schizophrenia, while unrelated to anhedonia, might include other risk factors.

The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis

BACKGROUND

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

METHODS

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

RESULTS

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3-4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3-4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

CONCLUSIONS

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials

Importance

Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random.

Objective

To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year.

Data Sources

Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020.

Study Selection

Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point.

Data Extraction and Synthesis

Baseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted.

Main Outcomes and Measures

Log SDs (bln σ̂) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in bln σ̂ between treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year.

Results

In the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (bln σ̂, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (bln σ̂, 1.11; 95% CI, 1.01-1.21; P = .02).

Conclusions and Relevance

Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.

Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis

Meta‐analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta‐analysis offers several advantages over meta‐analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment‐covariate interactions in an IPD meta‐analysis can lead to better estimates of patient‐specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta‐analysis (no variable selection, all treatment‐covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment‐covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient‐specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta‐analysis that aim to estimate patient‐specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided.

Association between Antidepressant Treatment during Pregnancy and Postpartum Self-Harm Ideation in Women with Psychiatric Disorders: A Cross-Sectional, Multinational Study

This study sought to estimate whether there is a preventative association between antidepressants during pregnancy and postpartum self-harm ideation (SHI), as this knowledge is to date unknown. Using the Multinational Medication Use in Pregnancy Study, we included a sample of mothers who were in the five weeks to one year postpartum period at the time of questionnaire completion, and reported preexisting or new onset depression and/or anxiety during pregnancy (n = 187). Frequency of postpartum SHI ('often/sometimes' = frequent, 'hardly ever' = sporadic, 'never') was measured via the Edinburgh Postnatal Depression Scale (EPDS) item 10, which reads "The thought of harming myself has occurred to me". Mothers reported their antidepressant use in pregnancy retrospectively. Overall, 52.9% of women took an antidepressant during pregnancy. Frequent SHI postpartum was reported by 15.2% of non-medicated women and 22.0% of women on past antidepressant treatment in pregnancy; this proportion was higher following a single trimester treatment compared to three trimesters (36.3% versus 18.0%). There was no preventative association of antidepressant treatment in pregnancy on reporting frequent SHI postpartum (weighted RR: 1.90, 95% CI: 0.79, 4.56), relative to never/hardly ever SHI. In a population of women with antenatal depression/anxiety, there was no preventative association between past antidepressant treatment in pregnancy and reporting frequent SHI in the postpartum year. This analysis is only a first step in providing evidence to inform psychiatric disorder treatment decisions for pregnant women.

Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression

BACKGROUND

Clinical trials have traditionally been analysed at the aggregate level, assuming that the group average would be applicable to all eligible and similar patients. We re-analyzed a mega-trial of antidepressant therapy for major depression to explore whether a multivariable prediction model may lead to different treatment recommendations for individual participants.

METHODS

The trial compared the second-line treatment strategies of continuing sertraline, combining it with mirtazapine or switching to mirtazapine after initial failure to remit on sertraline among 1,544 patients with major depression. The outcome was the Personal Health Questionnaire-9 (PHQ-9) at week 9: the original analyses showed that both combining and switching resulted in greater reduction in PHQ-9 by 1.0 point than continuing. We considered several models of penalized regression or machine learning.

RESULTS

Models using support vector machines (SVMs) provided the best performance. Using SVMs, continuing sertraline was predicted to be the best treatment for 123 patients, combining for 696 patients, and switching for 725 patients. In the last two subgroups, both combining and switching were equally superior to continuing by 1.2 to 1.4 points, resulting in the same treatment recommendations as with the original aggregate data level analyses; in the first subgroup, however, switching was substantively inferior to combining (-3.1, 95%CI: -5.4 to -0.5).

LIMITATIONS

Stronger predictors are needed to make more precise predictions.

CONCLUSIONS

The multivariable prediction models led to improved recommendations for a minority of participants than the group average approach in a megatrial.

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials

IMPORTANCE

Antidepressants are commonly used worldwide to treat major depressive disorder. Symptomatic response to antidepressants can vary depending on differences between individuals; however, this variability may reflect nonspecific or random factors.

OBJECTIVES

To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether this variability is associated with severity of major depressive disorder, antidepressant class, or year of study publication.

DATA SOURCES

Data used were from a recent network meta-analysis of acute treatment with licensed antidepressants in adults with major depressive disorder. The following databases were searched from inception to January 8, 2016: the Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycINFO. Additional sources were international trial registries, drug approval agency websites, and key scientific journals.

STUDY SELECTION

Analysis was restricted to double-blind, randomized placebo-controlled trials with available data at the study's end point.

DATA EXTRACTION AND SYNTHESIS

Baseline and end point means, SDs, number of participants in each group, antidepressant class, and publication year were extracted. The data were analyzed between August 14 and November 18, 2019.

MAIN OUTCOMES AND MEASURES

With the use of validated methods, coefficients of variation were derived for antidepressants and placebo, and their ratios were calculated to compare outcome variability between antidepressant and placebo. Ratios were entered into a random-effects model, with the expectation that response to antidepressants would be more variable than response to placebo. Analysis was repeated after stratifying by baseline severity of depression, antidepressant class (selective serotonin reuptake inhibitors: citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, and vilazodone; serotonin and norepinephrine reuptake inhibitors: desvenlafaxine and venlafaxine; norepinephrine-dopamine reuptake inhibitor: bupropion; noradrenergic agents: amitriptyline and reboxetine; and other antidepressants: agomelatine, mirtazapine, and trazodone), and publication year.

RESULTS

In the 87 eligible randomized placebo-controlled trials (17 540 unique participants), there was significantly more variability in response to antidepressants than to placebo (coefficients of variation ratio, 1.14; 95% CI, 1.11-1.17; P < .001). Baseline severity of depression did not moderate variability in response to antidepressants. Variability in response to selective serotonin reuptake inhibitors was lower than variability in response to noradrenergic agents (coefficients of variation ratio, 0.88; 95% CI, 0.80-0.97; P = .01), as was the variability in response to other antidepressants compared with noradrenergic agents (coefficients of variation ratio, 0.87; 95% CI, 0.79-0.97; P = .001). Variability also tended to be lower in studies that were published more recently, with coefficients of variation changing by a value of 0.005 (95% CI, 0.002-0.008; P = .003) for every year a study is more recent.

CONCLUSIONS AND RELEVANCE

Individual differences may be systematically associated with responses to antidepressants in major depressive disorder beyond placebo effects or statistical factors. This study provides empirical support for identifying moderators and personalizing antidepressant treatment.

Analysis of depressive episodes, their recurrence and pharmacologic treatment in primary care patients: A retrospective descriptive study

Background

Depression is one of the most prevalent health problems, frequently being a medium- and long-term condition, with a high comorbidity rate and with frequent relapses and recurrences. Although numerous studies have compared the effectiveness of specific antidepressant therapy drugs and have assessed relapses, scientific evidence on the relationship between pharmacologic treatments and recurrence is scarce. The objective of this study is to describe depressive episodes in a primary care patient cohort, the percentage of depression recurrences and the administered pharmacologic treatment, from a naturalistic perspective.

Methods

Retrospective descriptive study. 957 subjects were included. The dependent variable was a depression diagnosis and independent variables were: gender, age at time of data collection; age of onset, first-episode treatment, number of recurrences, age at recurrences, treatment prescribed for recurrences using therapeutic groups categorization.

Results

Recurrences are frequent, affecting more than 40% of the population. In the first episode, 13.69% of the patients were not prescribed pharmacological treatment, but this percentage decreased over the following depression episodes. 80.9% of the patients who did not receive drug treatment in the first depression episode did not experience subsequent episodes. Monotherapy, and specifically, SSRIs were the most frequently prescribed treatment option for all depressive episodes. Regards the combined pharmacologic treatment, the most frequent drug combinations were SSRIs and benzodiazepines.

Limitations

In order to increase the power of results, the statistical analysis was performed using therapeutic groups categorization, not individually analyzing each drug and dose.

Conclusions

Depressive episode recurrence is frequent in primary care patients. Further studies having a prospective design are needed in order to expand on this issue.

Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models

Importance

In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error.

Objective

To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder.

Design, Setting, and Participants

This retrospective cohort study included data from 81 630 adults with a coded diagnosis of major depressive disorder from 2 academic medical centers in Boston, Massachusetts, including outpatient primary and specialty care clinics from December 1, 1997, to December 31, 2017. Data were analyzed from January 1, 2018, to March 15, 2020.

Exposures

Treatment with at least 1 of 11 standard antidepressants.

Main Outcomes and Measures

Stable treatment response, intended as a proxy for treatment effectiveness, defined as continued prescription of an antidepressant for 90 days. Supervised topic models were used to extract 10 interpretable covariates from coded clinical data for stability prediction. With use of data from 1 hospital system (site A), generalized linear models and ensembles of decision trees were trained to predict stability outcomes from topic features that summarize patient history. Held-out patients from site A and individuals from a second hospital system (site B) were evaluated.

Results

Among the 81 630 adults (56 340 women [69%]; mean [SD] age, 48.46 [14.75] years; range, 18.0-80.0 years), 55 303 reached a stable response to their treatment regimen during follow-up. For held-out patients from site A, the mean area under the receiver operating characteristic curve (AUC) for discrimination of the general stability outcome was 0.627 (95% CI, 0.615-0.639) for the supervised topic model with 10 covariates. In evaluation of site B, the AUC was 0.619 (95% CI, 0.610-0.627). Building models to predict stability specific to a particular drug did not improve prediction of general stability even when using a harder-to-interpret ensemble classifier and 9256 coded covariates (specific AUC, 0.647; 95% CI, 0.635-0.658; general AUC, 0.661; 95% CI, 0.648-0.672). Topics coherently captured clinical concepts associated with treatment response.

Conclusions and Relevance

The findings suggest that coded clinical data available in electronic health records may facilitate prediction of general treatment response but not response to specific medications. Although greater discrimination is likely required for clinical application, the results provide a transparent baseline for such studies.

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient's unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.