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Comparative Study
. 2017 Apr 1;74(4):370-378.
doi: 10.1001/jamapsychiatry.2017.0025.

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

Adam M Chekroud  1 , Ralitza Gueorguieva  2 , Harlan M Krumholz  3 , Madhukar H Trivedi  4 , John H Krystal  5 , Gregory McCarthy  6
Affiliations
Comparative Study

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

Adam M Chekroud et al. JAMA Psychiatry. .

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 < .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, -0.7 to 0.8; P = .94).

Conclusions and relevance: Two 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.

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Conflict of interest statement

Conflict of Interest Disclosures: Mr Chekroud holds equity in Spring Health (doing business as Spring Care Inc), a behavioral health startup. He is lead inventor on a provisional patent submission by Yale University. Dr Gueorguieva discloses consulting fees for Palo Alto Health Sciences and Mathematica Policy Research and a provisional patent submission by Yale University (Y0087.70116US00). Dr Krumholz is a recipient of research agreements from Medtronic and Janssen (a pharmaceutical company of Johnson & Johnson), through Yale University, to develop methods of clinical trial data sharing; is the recipient of a grant from the US Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures; chairs (paid) a cardiac scientific advisory board for UnitedHealth; and is the founder of Hugo, a personal health information platform. Dr Trivedi has served as a paid adviser or consultant to Abbott, Abdi Ibrahim, Akzo (Organon), Alkermes, AstraZeneca, Axon Advisors, Bristol-Myers Squibb, Cephalon, Cerecor, Concert Pharmaceuticals, Eli Lilly, Evotec, Fabre Kramer Pharmaceuticals, Forest Pharmaceuticals, GlaxoSmithKline, Janssen Global Services, Janssen Pharmaceutical Products, Johnson & Johnson PRD, Libby, Lundbeck, Mead Johnson, MedAvante, Medtronic, Merck, Mitsubishi Tanabe Pharma Development America, Naurex, Neuronetics, Otsuka, Pamlab, Parke-Davis, Pfizer, PgxHealth, Phoenix Marketing Solutions, Rexahn Pharmaceuticals, Ridge Diagnostics, Roche Products, Sepracor, Shire Development, Sierra, SK Life and Science, Sunovion, Takeda, Tal Medical/Puretech Venture, Targacept, Transcept, VantagePoint, Vivus, and Wyeth-Ayerst Laboratories; he has received research support from the Agency for Healthcare Research and Quality, Corcept Therapeutics, Cyberonics, National Alliance for Research on Schizophrenia and Depression (now The Brain & Behavior Research Foundation), National Institute of Mental Health (NIMH), National Institute for Drug Abuse, Novartis, Pharmacia & Upjohn, Predix Pharmaceuticals (Epix), and Solvay. Dr Krystal is the editor of Biological Psychiatry. He has been a paid consultant to the following companies: LLC, AstraZeneca Pharmaceuticals, Biogen, Biomedisyn Corporation, Forum Pharmaceuticals, Janssen Pharmaceuticals, Orsuka America Pharmaceutical, Sunovion Pharmaceuticals, Takeda Industries, and Taisho Pharmaceutical Co. He is an unpaid member of the Scientific Advisory Board of Biohaven Pharmaceuticals, Blackthorn Therapeutics, Lohocla Research Corporation, Luc Therapeutices, Pfizer Pharmaceuticals, Spring Care, Inc, and TRImaran Pharma. He holds stock in ArRETT Neuroscience and Biohaven Pharmaceuticals and stock options in Blackthorn Therapeutics and Luc Therapeutics. Dr Krystal has the following patents and inventions: (1) dopamine and noradrenergic reuptake inhibitors in treatment of schizophrenia (patent No. 5,447,948); (2) co-inventor on a filed patent application by Yale University related to targeting the glutamatergic system for the treatment of neuropsychiatric disorders (PCTWO06108055A1); (3) intranasal administration of ketamine to treat depression (US application No. 14/197,767 and US application or Patient Cooperation Treaty international application No. 14/306,382); (4) composition and methods to treat addiction (provisional use patent application No. 61/973/961); and (5) treatment selection for major depressive disorder (US Patent and Trademark Office docket No. Y0087.70116US00). No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Data-Driven Decomposition of Depressive Checklists Using Hierarchical Clustering
This procedure sequentially groups symptoms according to the similarity of their responses across a patient cohort. With this procedure, groups of symptoms that merge at high values relative to the merge points of their subgroups are considered candidates for natural clusters. A and B, In the Quick Inventory of Depressive Symptomatology–Self Report (QIDS-SR) checklist, we identified an identical 3-cluster solution in both the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 4017) and Combining Medications to Enhance Depression Outcomes (CO-MED) trials (n = 640). C, A comparable symptom structure was also observed at baseline for STAR*D patients when measured according to the Hamilton Depression (HAM-D) rating scale. The names of the individual checklist items are colored according to their cluster assignment. Line lengths in the dendogram reflect how similar items or clusters are to one another (shorter line length indicates greater similarity).
Figure 2.
Figure 2.. Model-Fitted Outcome Trajectories for Each Symptom Cluster
A, Measured according to the Quick Inventory of Depressive Symptomatology–Self Report (QIDS-SR) checklist in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and Combining Medications to Enhance Depression Outcomes (CO-MED) trials (12 weeks). B, Measured according to the Hamilton Depression (HAM-D) rating scale in 7 phase 3, placebo-controlled trials of duloxetine (8 weeks). The y-axes represent mean severity within a cluster and so should be multiplied by the number of symptoms within a cluster to convert to original units.
Figure 3.
Figure 3.. Use of Machine Learning to Predict Outcomes Specific to Each Symptom Cluster
For each symptom cluster, a new model was trained on patients who received citalopram in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (A). After cross-validation, we applied the models to patients in 3 treatment arms of the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (B) to test their ability to generalize to an independent clinical trial sample. Core emotional symptoms could be predicted with significantly above-chance performance in the escitalopram with placebo and venlafaxine with mirtazapine arms. Sleep/insomnia symptoms could be predicted above chance for escitalopram with bupropion.
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