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; Muredach P Reilly; Marylyn D Ritchie; Nicholas P Tatonetti; Julia Wattacheril

doi:10.1111/cts.70105

Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine-based approach in diverse healthcare systems

Clin Transl Sci. 2025 Jan;18(1):e70105. doi: 10.1111/cts.70105.

Authors

Anna O Basile^{1

2}, Anurag Verma³, Leigh Anne Tang⁴, Marina Serper⁵, Andrew Scanga⁶, Ava Farrell⁷, Brittney Destin⁸, Rotonya M Carr⁹, Anuli Anyanwu-Ofili¹⁰, Gunaretnam Rajagopal^{10

11}, Abraham Krikhely⁸, Marc Bessler⁸, Muredach P Reilly^{12

13}, Marylyn D Ritchie¹⁴, Nicholas P Tatonetti^{1

15

16}, Julia Wattacheril¹⁷

Affiliations

¹ Department of Biomedical Informatics, Columbia University, New York, New York, USA.
² Department of Computational Biology, New York Genome Center, New York, New York, USA.
³ Division of Translational Medicine and Human Genetics, Department of Medicine, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
⁴ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁵ Division of Gastroenterology and Hepatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
⁶ Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁷ Division of Critical Care, Department of Pediatrics, New York Presbyterian Morgan Stanley Children's Hospital of New York, New York, New York, USA.
⁸ Division of Metabolic and Bariatric Surgery, Department of Surgery, Columbia University Irving Medical Center, New York, New York, USA.
⁹ Division of Gastroenterology, Department of Medicine, University of Washington, Seattle, Washington, USA.
¹⁰ Johnson & Johnson Innovative Medicine, Spring House, Pennsylvania, USA.
¹¹ Samsara BioCapital, Palo Alto, California, USA.
¹² Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA.
¹³ Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA.
¹⁴ Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
¹⁵ Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
¹⁶ Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California, USA.
¹⁷ Division of Digestive and Liver Diseases, Department of Medicine, Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York, New York, USA.

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.

MeSH terms

Adult
Aged
Algorithms*
Databases, Factual / statistics & numerical data
Electronic Health Records* / statistics & numerical data
Female
Humans
Liver Cirrhosis / diagnosis
Male
Middle Aged
Non-alcoholic Fatty Liver Disease* / diagnosis
Phenotype*

Abstract

MeSH terms

Grants and funding