Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems
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 dise...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Clinical and Translational Science |
| Online Access: | https://doi.org/10.1111/cts.70105 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Anna O. Basile |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-db47e59a8ad34622bbc94a05869d9747 |
| institution | Kabale University |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-db47e59a8ad34622bbc94a05869d97472025-01-24T08:17:46ZengWileyClinical and Translational Science1752-80541752-80622025-01-01181n/an/a10.1111/cts.70105Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systemsAnna O. Basile0Anurag Verma1Leigh Anne Tang2Marina Serper3Andrew Scanga4Ava Farrell5Brittney Destin6Rotonya M. Carr7Anuli Anyanwu‐Ofili8Gunaretnam Rajagopal9Abraham Krikhely10Marc Bessler11Muredach P. Reilly12Marylyn D. Ritchie13Nicholas P. Tatonetti14Julia Wattacheril15Department of Biomedical Informatics Columbia University New York New York USADivision of Translational Medicine and Human Genetics, Department of Medicine, Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USADivision of Gastroenterology and Hepatology University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine Vanderbilt University Medical Center Nashville Tennessee USADivision of Critical Care, Department of Pediatrics New York Presbyterian Morgan Stanley Children's Hospital of New York New York New York USADivision of Metabolic and Bariatric Surgery, Department of Surgery Columbia University Irving Medical Center New York New York USADivision of Gastroenterology, Department of Medicine University of Washington Seattle Washington USAJohnson & Johnson Innovative Medicine Spring House Pennsylvania USAJohnson & Johnson Innovative Medicine Spring House Pennsylvania USADivision of Metabolic and Bariatric Surgery, Department of Surgery Columbia University Irving Medical Center New York New York USADivision of Metabolic and Bariatric Surgery, Department of Surgery Columbia University Irving Medical Center New York New York USAIrving Institute for Clinical and Translational Research Columbia University New York New York USADepartment of Genetics, Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Biomedical Informatics Columbia University New York New York USADivision of Digestive and Liver Diseases, Department of Medicine, Center for Liver Disease and Transplantation Columbia University Irving Medical Center New York New York USAAbstract 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.https://doi.org/10.1111/cts.70105 |
| spellingShingle | 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 Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems Clinical and Translational Science |
| title | Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems |
| title_full | Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems |
| title_fullStr | Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems |
| title_full_unstemmed | Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems |
| title_short | Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems |
| title_sort | rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine based approach in diverse healthcare systems |
| url | https://doi.org/10.1111/cts.70105 |
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