CoChat
Sign Up
Cited by 676Treatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.

Cross-trial prediction of treatment outcome in depression: a machine learning approach

Chekroud, Adam Mourad, Zotti, Ryan Joseph, Shehzad, Zarrar, Gueorguieva, Ralitza, Johnson, Marcia K, Trivedi, Madhukar H, Cannon, Tyrone D, Krystal, John Harrison, Corlett, Philip Robert·2016·The Lancet Psychiatry
DOI 10.1016/s2215-0366(15)00471-x
Open this paper in CoChat — free account

Abstract

Background Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.Methods We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).Findings We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.Interpretation Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.Funding Yale University.

Overview

Integrity signals

Registry

Crossref · 10.1016/s2215-0366(15)00471-x

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 49 references

Cited by

Sorted by citations

Items 601–674 of 674

Related

See all

Metadata

Authors
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R.
Affiliations
  • Yale New Haven Hospital (US)
  • Capital One (United States) (US)
  • Yale University (US)
  • The University of Texas Southwestern Medical Center (US)
Corresponding author
Chekroud, Adam Mourad
ORCID
Venue
The Lancet Psychiatry
Publisher
Elsevier BV
Published
2016-03
Volume
3
Issue
3
Pages
243-250
ISSN
2215-0366 (ISSN-L)
DOI
10.1016/s2215-0366(15)00471-x
OpenAlex
W2257438637
Cited by
676
References
49
Citations by year
2026: 57 · 2025: 86 · 2024: 53 · 2023: 70 · 2022: 74 · 2021: 79 · 2020: 87 · 2019: 76 · 2018: 42 · 2017: 34 · 2016: 17
FWCI
45.64
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • The Lancet Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
depression, antidepressant treatment response prediction, citalopram, escitalopram, clinical remission, STAR*D, venlafaxine, mirtazapine, patient-reported outcomes, treatment matching, machine learning prediction models, external validation
MeSH terms
Machine Learning, Adolescent, Adult, Aged, Algorithms, Antidepressive Agents, Major Depressive Disorder, Humans, Middle Aged, Models, Statistical, Citalopram, Treatment Outcome, Cross-Over Studies, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review