ArticleJMIR mental health2026
Electronic Health Record-Based Phenotyping for Obsessive-Compulsive Disorder: Algorithm Development and Multicenter Validation Study.
Article in JMIR mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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24 authors.
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Abstract
Background: Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its substantial burden and moderate heritability, the etiology of OCD remains poorly understood, and treatments are often suboptimal. Although recent genome-wide association studies (GWAS) have identified some risk loci, much of the genetic architecture of OCD remains undiscovered, underscoring the need for scalable approaches to identify large, well-defined patient cohorts. Objective: This study aimed to develop and validate a scalable electronic health record (EHR)-based phenotyping algorithm for identifying OCD cases to support large-scale genetic and translational research. Methods: We leveraged EHR-linked biobank data from 2 large hospital systems, namely Vanderbilt University Medical Center (VUMC) and Mass General Brigham (MGB), to develop a high-throughput phenotyping algorithm integrating diagnostic codes, medication records, and natural language processing (NLP) of clinical notes. Algorithm performance was evaluated through expert chart review, and genetic analyses were performed in individuals of European genetic ancestry using the polygenic scores (PGS) of OCD, major depressive disorder (MDD), and height derived from the most recent GWAS. Results: Expert chart reviews demonstrated our algorithm combining both Conclusions: This study presents a scalable and cost-efficient EHR-based approach for identifying OCD cases across health systems. The algorithm achieves high PPV, and among individuals of European genetic ancestry, algorithm-defined cases show significant OCD PGS enrichment, supporting its utility for large-scale genetic studies and advancing understanding of the disorder's complex etiology.
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