JMIR mental health · 2026
Electronic Health Record-Based Phenotyping for Obsessive-Compulsive Disorder: Algorithm Development and Multicenter Validation Study.
СтатьяВалидационное исследованиеМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Bo, Miller-Fleming Tyne W, Yu Dongmei, Hucks Donald, Gantz Emily, Johnston Rebecca, Maxwell-Horn Angela, Cox Nancy, Sutcliffe James, Mathews Carol A, McArthur Evonne, McGuire Patrick L, Kabir Dia, Dankese Ashley, Giangrande Evan J, Fortgang Rebecca G, Wang Shirley B, Karmacharya Rakesh, Roffman Joshua L, Scharf Jeremiah M, Smoller Jordan W, Soda Takahiro, Crowley James J, Davis Lea K
Аннотация
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 International Statistical Classification of Diseases (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 reduced case yield. At both sites, algorithm-defined OCD cases of European genetic ancestry showed significantly higher OCD PGS than controls. In sensitivity analyses adjusting for MDD status, OCD PGS associations were more robust than MDD PGS associations, while height PGS showed no association, supporting the genetic plausibility and relative specificity 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 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.
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 International Statistical Classification of Diseases (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 reduced case yield. At both sites, algorithm-defined OCD cases of European genetic ancestry showed significantly higher OCD PGS than controls. In sensitivity analyses adjusting for MDD status, OCD PGS associations were more robust than MDD PGS associations, while height PGS showed no association, supporting the genetic plausibility and relative specificity 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 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.