ArticleAlcohol and alcoholism (Oxford, Oxfordshire)2025
Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data.
Article in Alcohol and alcoholism (Oxford, Oxfordshire), 2025. 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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Abstract
backgroundAlcohol Use Disorder (AUD) drives significant morbidity through alcohol-related liver disease. Accurate AUD identification in electronic health records is critical for research and care delivery, yet International Classification of Diseases (ICD) code-based algorithms miss many cases while manual review is impractical at scale. Computable phenotypes (CPs) integrating structured and unstructured EHR data offer a scalable solution.
methodsUsing University of Florida Health's Integrated Data Repository covering two million patients, we developed AUD CPs through a two-step process. First, candidate cohorts were identified using AUD-related ICD codes, medications, and keyword searches across structured and unstructured data. Second, rule-based combinations were iteratively refined through manual chart review. Final algorithms were evaluated against gold-standard chart review, measuring sensitivity, positive predictive value (PPV), and F1-score, then validated in an independent testing set and an external dataset.
resultsThe F1-optimized CP achieved an F1-score of .87 (sensitivity: .98, PPV: .78) in the testing set, while the precision-optimized CP achieved PPV of .9 (sensitivity: .68, F1-score: .77). Minimal performance attenuation between training and testing sets demonstrated robustness and generalizability. Both CPs substantially outperformed restricted AUD-specific ICD code-based approaches.
conclusionsCPs integrating structured and unstructured EHR data enable accurate, reproducible AUD identification, surpassing traditional AUD-specific ICD-based methods. This approach facilitates efficient cohort construction for clinical research, public health surveillance, and quality improvement initiatives targeting AUD and its consequences, addressing a critical gap in identifying patients who may benefit from screening and intervention.
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