Dataset Information

Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.

ABSTRACT: BACKGROUND:Improvement in depression within the first 2 weeks of antidepressant treatment predicts good outcomes, but non-improvers can still respond or remit, whereas improvers often do not.AimsWe aimed to investigate whether early improvement of individual depressive symptoms better predicts response or remission. METHOD:We obtained individual patient data of 30 trials comprising 2184 placebo-treated and 6058 antidepressant-treated participants. Primary outcome was week 6 response; secondary outcomes were week 6 remission and week 12 response and remission. We compared models that only included improvement in total score by week 2 (total improvement model) with models that also included improvement in individual symptoms. RESULTS:For week 6 response, the area under the receiver operating characteristic curve and negative and positive predictive values of the total improvement model were 0.73, 0.67 and 0.74 compared with 0.77, 0.70 and 0.71 for the item improvement model. Model performance decreased for week 12 outcomes. Of predicted non-responders, 29% actually did respond by week 6 and 43% by week 12, which was decreased from the baseline (overall) probabilities of 51% by week 6 and 69% by week 12. In post hoc analyses with continuous rather than dichotomous early improvement, including individual items did not enhance model performance. CONCLUSIONS:Examining individual symptoms adds little to the predictive ability of early improvement. Additionally, early non-improvement does not rule out response or remission, particularly after 12 rather than 6 weeks. Therefore, our findings suggest that routinely adapting pharmacological treatment because of limited early improvement would often be premature.Declaration of interestNone.

SUBMITTER: de Vries YA

PROVIDER: S-EPMC7557872 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.

de Vries Ymkje Anna YA Roest Annelieke M AM Bos Elisabeth H EH Burgerhof Johannes G M JGM van Loo Hanna M HM de Jonge Peter P

The British journal of psychiatry : the journal of mental science 20180628 1

<h4>Background</h4>Improvement in depression within the first 2 weeks of antidepressant treatment predicts good outcomes, but non-improvers can still respond or remit, whereas improvers often do not.AimsWe aimed to investigate whether early improvement of individual depressive symptoms better predicts response or remission.<h4>Method</h4>We obtained individual patient data of 30 trials comprising 2184 placebo-treated and 6058 antidepressant-treated participants. Primary outcome was week 6 respon ...[more]

PMID: 29952277

Similar Datasets

Project description:ContextAntidepressant medications represent the best established treatment for major depressive disorder, but there is little evidence that they have a specific pharmacological effect relative to pill placebo for patients with less severe depression.ObjectiveTo estimate the relative benefit of medication vs placebo across a wide range of initial symptom severity in patients diagnosed with depression.Data sourcesPubMed, PsycINFO, and the Cochrane Library databases were searched from January 1980 through March 2009, along with references from meta-analyses and reviews.Study selectionRandomized placebo-controlled trials of antidepressants approved by the Food and Drug Administration in the treatment of major or minor depressive disorder were selected. Studies were included if their authors provided the requisite original data, they comprised adult outpatients, they included a medication vs placebo comparison for at least 6 weeks, they did not exclude patients on the basis of a placebo washout period, and they used the Hamilton Depression Rating Scale (HDRS). Data from 6 studies (718 patients) were included.Data extractionIndividual patient-level data were obtained from study authors.ResultsMedication vs placebo differences varied substantially as a function of baseline severity. Among patients with HDRS scores below 23, Cohen d effect sizes for the difference between medication and placebo were estimated to be less than 0.20 (a standard definition of a small effect). Estimates of the magnitude of the superiority of medication over placebo increased with increases in baseline depression severity and crossed the threshold defined by the National Institute for Clinical Excellence for a clinically significant difference at a baseline HDRS score of 25.ConclusionsThe magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial.

Project description:Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding (GE) - which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.

Project description:INTRODUCTION:Cognitive behavioural therapies (CBTs) are one of the most effective treatments for major depression. However, ~50% of individuals do not adequately respond to intervention and of those who do remit from a depressive episode, over 50% will experience later relapse. Identification of patient-level factors which moderate treatment response may ultimately help to identify cognitive barriers that could be targeted to improve treatment efficacy. This individual patient data meta-analysis explores one such potential moderator-the ability to retrieve specific, detailed memories of the autobiographical past-as cognitive-based therapeutic techniques draw heavily on the ability to use specific autobiographical information to challenge the dysfunctional beliefs which drive depression. METHODS AND ANALYSIS:We have formed a collaborative network which will contribute known datasets. This will be supplemented by datasets identified through literature searches in Medline, PsycInfo, Web of Science, the Cochrane Central Register of Controlled Trials and WHO trials database between December 2018 and February 2019. Inclusion criteria are delivery of a cognitive or cognitive behavioural therapy for major depression, and measurement of autobiographical memory retrieval at preintervention. Primary outcomes are depressive symptoms and clinician-rated diagnostic status at postintervention, along with autobiographical memory specificity at postintervention. Secondary outcomes will consider each of these variables at follow-up. All analyses will be completed using random-effects models employing restricted maximum likelihood estimation. Risk of bias in included studies will be measured using the Revised Cochrane Risk of Bias Tool. ETHICS AND DISSEMINATION:The findings will be published in a peer-reviewed journal. Study results will contribute to better understanding of the role of autobiographical memory in patient response to CBTs, and may help to inform personalised medicine approaches to treatment of depression. PROSPERO REGISTRATION NUMBER:CRD42018109673.

Project description:ObjectivePoor diet can be detrimental to mental health. However, the overall evidence for the effects of dietary interventions on mood and mental well-being has yet to be assessed. We conducted a systematic review and meta-analysis examining effects of dietary interventions on symptoms of depression and anxiety.MethodsMajor electronic databases were searched through March 2018 for all randomized controlled trials of dietary interventions reporting changes in symptoms of depression and/or anxiety in clinical and nonclinical populations. Random-effects meta-analyses were conducted to determine effect sizes (Hedges' g with 95% confidence intervals [CI]) for dietary interventions compared with control conditions. Potential sources of heterogeneity were explored using subgroups and meta-regression analyses.ResultsResults: Sixteen eligible randomized controlled trials (published in English) with outcome data for 45,826 participants were included; the majority of which examined samples with nonclinical depression (n = 15 studies). Nonetheless, dietary interventions significantly reduced depressive symptoms (g = 0.162, 95% CI = 0.055 to 0.269, p = 0.003). Similar effects were observed among high-quality trials (g = 0.171, 95% C.I.=0.057 to 0.286, p=0.003) and when compared with both inactive (g = 0.114, 95% C.I.=0.008 to 0.219, p=0.035) and active controls (g = 0.224, 95% C.I.= 0.052 to 0.397, p = 0.011). No effect of dietary interventions was observed for anxiety (k = 11, n = 2270, g = 0.085, 95% C.I. = -0.031 to 0.202, p=0.151). Studies with female samples observed significantly greater benefits from dietary interventions, for symptoms of both depression and anxiety.ConclusionsDietary interventions hold promise as a novel intervention for reducing symptoms of depression across the population. Future research is required to determine the specific components of dietary interventions that improve mental health, explore underlying mechanisms, and establish effective schemes for delivering these interventions in clinical and public health settings.RegistrationPROSPERO Online Protocol: CRD42018091256.

Project description:BackgroundSelf-monitoring of blood pressure (BP) appears to reduce BP in hypertension but important questions remain regarding effective implementation and which groups may benefit most. This individual patient data (IPD) meta-analysis was performed to better understand the effectiveness of BP self-monitoring to lower BP and control hypertension.Methods and findingsMedline, Embase, and the Cochrane Library were searched for randomised trials comparing self-monitoring to no self-monitoring in hypertensive patients (June 2016). Two reviewers independently assessed articles for eligibility and the authors of eligible trials were approached requesting IPD. Of 2,846 articles in the initial search, 36 were eligible. IPD were provided from 25 trials, including 1 unpublished study. Data for the primary outcomes-change in mean clinic or ambulatory BP and proportion controlled below target at 12 months-were available from 15/19 possible studies (7,138/8,292 [86%] of randomised participants). Overall, self-monitoring was associated with reduced clinic systolic blood pressure (sBP) compared to usual care at 12 months (-3.2 mmHg, [95% CI -4.9, -1.6 mmHg]). However, this effect was strongly influenced by the intensity of co-intervention ranging from no effect with self-monitoring alone (-1.0 mmHg [-3.3, 1.2]), to a 6.1 mmHg (-9.0, -3.2) reduction when monitoring was combined with intensive support. Self-monitoring was most effective in those with fewer antihypertensive medications and higher baseline sBP up to 170 mmHg. No differences in efficacy were seen by sex or by most comorbidities. Ambulatory BP data at 12 months were available from 4 trials (1,478 patients), which assessed self-monitoring with little or no co-intervention. There was no association between self-monitoring and either lower clinic or ambulatory sBP in this group (clinic -0.2 mmHg [-2.2, 1.8]; ambulatory 1.1 mmHg [-0.3, 2.5]). Results for diastolic blood pressure (dBP) were similar. The main limitation of this work was that significant heterogeneity remained. This was at least in part due to different inclusion criteria, self-monitoring regimes, and target BPs in included studies.ConclusionsSelf-monitoring alone is not associated with lower BP or better control, but in conjunction with co-interventions (including systematic medication titration by doctors, pharmacists, or patients; education; or lifestyle counselling) leads to clinically significant BP reduction which persists for at least 12 months. The implementation of self-monitoring in hypertension should be accompanied by such co-interventions.

Project description:BackgroundNumerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques.MethodsWe provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power.ResultsThe primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions.ConclusionThis paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

Dataset Information

Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.

Publications

Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets