Evidence map›Paper›PMID 38565338›Full record

ArticleJournal of affective disorders2024

Identifying data-driven subtypes of major depressive disorder with electronic health records.

Abhishek Sharma, Pilar F Verhaak, Thomas H McCoy, Roy H Perlis, Finale Doshi-Velez

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Article in Journal of affective disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Abhishek SharmaHarvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, United States of America.
Pilar F VerhaakCenter for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America.
Thomas H McCoyCenter for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States of America.
Roy H PerlisCenter for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States of America. Electronic address: rperlis@mgh.harvard.edu.
Finale Doshi-VelezHarvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, United States of America. Electronic address: finale@seas.harvard.edu.

Funding

Data-driven subtyping in major depressive disorderR01MH123804 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PERLIS, ROY H. · 2021 to 2024
$3.1M
NIMH NIH HHS R01 MH123804
6 · The paper itself

Abstract

backgroundEfforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration.

methodsWe utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups.

resultsAmong 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions.

conclusionThese data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Indexed as

Electronic Health RecordsMajor Depressive DisorderSelective Serotonin Reuptake InhibitorsAdultComorbidityFemaleHumansMaleMassachusettsMiddle AgedSerotonin and Noradrenaline Reuptake InhibitorsSelective Serotonin Reuptake InhibitorsSerotonin and Noradrenaline Reuptake InhibitorsData-driven subtypesHeterogeneityLatent Dirichlet allocationMajor depressive disorderRepresentation learning

Identifiers

PMID38565338
PMCPMC13464343

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