Rapid Identification and Phenotyping of Nonalcoholic Fatty Liver Disease Patients Using an Algorithmic Approach in Diverse, Urban Healthcare Systems
Authors: Basile Anna O. , Verma Anurag , Tang Leigh Anne , Serper Marina , Scanga Andrew , Farrell Ava , Destin Brittney , Carr Rotonya M. , Anyanwu-Ofili Anuli , Rajagopal Gunaretnam , Krikhely Abraham , Bessler Marc , Reilly Muredach P. , Ritchie Marylyn D. , Tatonetti Nicholas P. , Wattacheril Julia 
Published: April 2021

AbstractObjectivesNonalcoholic Fatty Liver Disease (NAFLD) is the most common global cause of chronic liver disease. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to accurately recognize and stratify at-risk patients. 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.MethodsWe present a rule-based phenotyping algorithm for the rapid 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 multiple structured and unstructured data elements, including diagnosis codes, laboratory measurements, radiology and pathology modalities.ResultsOur approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) of whom were 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%.ConclusionsOur rule-based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub-phenotyping NAFLD patients within a large EHR system.Study HighlightsWHAT IS KNOWNNAFLD is the leading form of chronic liver disease with a rising prevalence in the population.NAFLD is often under-recognized in at-risk individuals, including within healthcare settings.Current means of identification and stratification are complex and dependent on provider recognition of clinical risk factors.WHAT IS NEW HEREAn accurate, validated rule-based algorithm for the high-throughput and rapid EHR identification of NAFLD patients.Rapid discovery of a NAFLD cohort from diverse EHR systems comprising approximately 12.1 million patients.Our algorithm has high performance (mean PPV=85%, sensitivity=79.6%) in NAFLD patient discovery.The majority of algorithmically derived NAFLD patients were previously unidentified within healthcare systems.Computational stratification of individuals with advanced fibrosis can be achieved rapidly.