Dataset Information

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.

ABSTRACT:

Importance

Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small.

Objectives

To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups.

Design, setting, and participants

Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n?=?4039) were used to identify clusters of symptoms in a depressive symptom checklist. The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n?=?640). Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n?=?4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n?=?2515). Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016.

Main outcomes and measures

Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms.

Results

Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years. Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale. Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P?Conclusions and relevanceTwo common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.

SUBMITTER: Chekroud AM

PROVIDER: S-EPMC5863470 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.

Chekroud Adam M AM Gueorguieva Ralitza R Krumholz Harlan M HM Trivedi Madhukar H MH Krystal John H JH McCarthy Gregory G

JAMA psychiatry 20170401 4

<h4>Importance</h4>Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small.<h4>Objectives</h4>To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and ...[more]

PMID: 28241180

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Project description:ObjectiveThe aim of this study is to create a rank order of the comparative efficacy and acceptability (risk of all-cause discontinuation) of antidepressant treatment in poststroke depression (PSD) by integrating direct and indirect evidence.DesignMultiple-treatments meta-analysis of randomised controlled trials.ParticipantsPatients with depression following stroke.Interventions10 antidepressants and placebo in the acute treatment of PSD.Outcome measuresThe primary outcomes were the overall efficacy, defined as the mean change of the total depression score. The secondary outcome was the acceptability, defined as risk of all-cause discontinuation. These estimates as standardised mean differences or ORs with 95% CIs.ResultsWe identified 12 suitable trials, with data from 707 participants. All drugs were significantly more effective than placebo apart from sertraline, nefiracetam and fluoxetine. Most of the comparisons for acceptability revealed no significant differences except that paroxetine had significantly lower all-cause discontinuation than doxepin, citalopram and fluoxetine. Standardised mean differences compared with placebo for efficacy varied from -6.54 for the best drug (reboxetine) to 0.51 for the worst drug (nefiracetam). ORs compared with placebo for acceptability ranged from 0.09 for the best drug (paroxetine) to 3.42 for the worst drug (citalopram). For the efficacy rank, reboxetine, paroxetine, doxepin and duloxetine were among the most efficacious treatments, the cumulative probabilities of which were 100%, 85.7%, 83.2%, 62.4%, respectively. With respect to the acceptability rank, paroxetine, placebo, sertraline and nortriptyline were among the most acceptable treatments, the cumulative probabilities of which were 92.4%, 63.5%, 57.3%, 56.3%.ConclusionAfter weighing the efficacy and acceptability, we conclude that paroxetine might be the best choice when starting acute treatment for PSD, and fluoxetine might be the worst choice.Trial registration numberThis systematic review has been registered in the Prospective Register of Systematic Review Protocols (PROSPERO) public database (CRD42017054741; http://www.crd.york.ac.uk/PROSPERO).

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Dataset Information

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.

Importance

Objectives

Design, setting, and participants

Main outcomes and measures

Results

Publications

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.

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