Identifying data-driven subtypes of major depressive disorder with electronic health records.
J Affect Disord
; 356: 64-70, 2024 Jul 01.
Article
em 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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Inibidores Seletivos de Recaptação de Serotonina
/
Transtorno Depressivo Maior
/
Registros Eletrônicos de Saúde
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article