Rapid Identification and Phenotyping of Nonalcoholic Fatty Liver Disease Patients Using an Algorithmic Approach in Diverse, Urban Healthcare Systems

Objectives Nonalcoholic 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. Methods We 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. Results Our 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%. Conclusions Our rule-based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub-phenotyping NAFLD patients within a large EHR system. WHAT IS KNOWN WHAT IS NEW HERE ### Competing Interest Statement JW has received research support from Janssen, Galectin, Intercept, Genfit, Shire, Conatus, Zydus, and has served on the advisory board for Astra Zeneca/MedImmune, AMRA. RMC has received research support from Intercept Pharmaceuticals and Merck, Inc. MS is funded by NIDDK R01DK132138, R01DK131547 and has an unrestricted grant from Grifols, SA. GR retired from Janssen Pharma R&D as Scientific Fellow and Head of Computational Sciences and is currently a Venture Partner in Samsara BioCapital, Palo Alto, CA. AK is a speaker and proctor for Intuitive, reviewer for surgical videos for Crowd Sourced Assessment of Technical Skills (CSATs), and a consultant for Johnson and Johnson and Surgical Specialties Corporation. AV, LT, AS, AF, BD, AA, GR, AK, MB, MPR, MDR, JD, and NPT have nothing to disclose. Patent for algorithm to Columbia University Trustees; 2021 The Trustees of Columbia University in the City of New York. The owner has no objection to reproduction of the work for academic non-commercial purposes, but otherwise reserves all copyright rights whatsoever. JW, NPT and AOB are co-inventors. ### Funding Statement Funding was provided by Janssen Research and Development in collaboration with Columbia University Irving Medical Center. The sponsor was involved in study concept and design. This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health (NIH). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Columbia University Institutional Review Boards of Columbia University Irving Medical Center, University of Pennsylvania Health System and Vanderbilt University Medical Center. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data in the study were extracted from patient electronic health records at Columbia University Irving Medical Center,University of Pennsylvania Healthcare System (UPHS), and Vanderbilt Medical Center (VUMC). These data are not available for public use due to institutional privacy policies and federal regulations. * EHR : Electronic Health Record NAFLD : nonalcoholic fatty liver disease NASH : nonalcoholic steatohepatitis OMOP : Observational Medical Outcomes Partnership CDM : Common Data Model OHDSI : Observational Health Data Sciences and Informatics A1c : Glycated hemoglobin T2D : Type 2 Diabetes DOB : Date of Birth CUIMC : Columbia University Irving Medical Center UPHS : University of Pennsylvania Health System VUMC : Vanderbilt University Medical Center MRN : Medical Record Number FIB-4 : Fibrosis-4 APRI : Aspartate transaminase to Platelet Ratio Index NIT : non-invasive test