Identifying data-driven subtypes of major depressive disorder with electronic health records.
J Affect Disord
; 356: 64-70, 2024 Jul 01.
Article
in En
| MEDLINE
| ID: mdl-38565338
ABSTRACT
BACKGROUND:
Efforts 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.METHODS:
We 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.RESULTS:
Among 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.CONCLUSION:
These 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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Selective Serotonin Reuptake Inhibitors
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Depressive Disorder, Major
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Electronic Health Records
Limits:
Adult
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Female
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Humans
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Male
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Middle aged
Country/Region as subject:
America do norte
Language:
En
Journal:
J Affect Disord
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: