JMIR Publications

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 two 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 validation was performed using OCD polygenic risk scores (PRS) derived from the most recent OCD GWAS.

Results:

Expert chart reviews demonstrated our algorithm combining both ICD codes and NLP achieved the highest positive predictive values (PPV) for OCD case identification (0.84 at VUMC; 0.91 at MGB) compared to using either ICD codes or NLP alone, albeit with a reduced case yield. Furthermore, at both sites, algorithm-determined cases exhibited significantly elevated PRS relative to controls, providing genetic validation of the phenotype.

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 demonstrates genetic validity, supporting its utility for large-scale genetic studies and advancing understanding of the disorder’s complex etiology.