Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems - Anna O. Basile; Anurag Verma; Leigh Anne Tang; Marina Serper; Andrew Scanga; Ava Farrell; Brittney Destin; Rotonya M. Carr; Anuli Anyanwu‐Ofili; Gunaretnam Rajagopal; Abraham Krikhely; Marc Bessler; et al. (Clinical and Translational Science)
Abstract Nonalcoholic fatty liver disease (NAFLD) is the most common global cause of chronic liver disease and remains under‐recognized within healthcare systems. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to recognize and stratify at‐risk patients accurately. Our work aims to rapidly identify NAFLD patients within large electronic health record (EHR) databases for automated stratification and targeted intervention based on clinically relevant phenotypes. We present a rule‐based phenotyping algorithm for efficient identification of NAFLD patients developed using EHRs from 6.4 million patients at Columbia University Irving Medical Center (CUIMC) and validated at two independent healthcare centers. The algorithm uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model and queries structured and unstructured data elements, including diagnosis codes, laboratory measurements, and radiology and pathology modalities. Our approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) previously unidentifiable by NAFLD diagnosis codes. Fibrosis scoring on patients without histology identified 943 subjects with scores indicative of advanced fibrosis (FIB‐4, APRI, NAFLD–FS). The algorithm was validated at two independent healthcare systems, University of Pennsylvania Health System (UPHS) and Vanderbilt Medical Center (VUMC), where 20,779 and 19,575 NAFLD patients were identified, respectively. Clinical chart review identified a high positive predictive value (PPV) across all healthcare systems: 91% at CUIMC, 75% at UPHS, and 85% at VUMC, and a sensitivity of 79.6%. Our rule‐based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub‐phenotyping NAFLD patients within a large EHR system.