Sarjukumar A Panchal1, David E Kaplan2,3, David S Goldberg4, Nadim Mahmud5,6,7,8. 1. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 2. Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, 4th Floor, South Pavilion, Philadelphia, PA, 19104, USA. 3. Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA. 4. Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA. 5. Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, 4th Floor, South Pavilion, Philadelphia, PA, 19104, USA. nadim@pennmedicine.upenn.edu. 6. Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA. nadim@pennmedicine.upenn.edu. 7. Leonard David Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. nadim@pennmedicine.upenn.edu. 8. Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA. nadim@pennmedicine.upenn.edu.
Abstract
BACKGROUND: Alcoholic hepatitis (AH) is a clinically diagnosed syndrome with high short-term mortality for which liver transplantation may be curative. A lack of validated algorithms to identify AH hospitalizations has hindered clinical epidemiology research. METHODS: This was a retrospective cohort study of patients with cirrhosis using Veterans Health Administration (VHA) data from 2008 to 2015. We randomly sampled hospitalizations based upon abnormal liver tests and administrative codes for acute hepatitis or alcohol-associated liver disease (ALD). Hospitalizations were manually adjudicated for AH per society guidelines. A priori algorithms were evaluated to compute positive predicted value (PPV) and positive likelihood ratio (LR+), and were tested in an external University of Pennsylvania Health System (UPHS) cohort. RESULTS: Of 368 hospitalizations, 142 (38.6%) were adjudicated as AH. AH patients were younger (55 vs. 58 years, p < 0.001), less likely to have prior cirrhosis decompensation (57% vs. 73.9%, p < 0.001), and had higher AST-to-ALT ratios (median 2.9 vs. 1.9 mg/dL, p < 0.001) and higher bilirubin levels (median 2.9 vs. 1.9 mg/dL, p < 0.001). Algorithms combining clinical laboratory criteria (AST > 85 U/L but < 450 U/L, AST-to-ALT ratio > 2, total bilirubin > 5 mg/dL) and administrative coding criteria yielded the highest PPV (96.4%, 95% CI 87.7-99.6) and the highest LR+ (43.0, 95% CI 10.6-173.5). Several algorithms demonstrated 100% PPV for definite AH in the UPHS external cohort. CONCLUSION: We have identified algorithms for AH hospitalizations with excellent PPV and LR+. These high-specificity algorithms may be used in VHA datasets to identify patients with high likelihood of AH, but should not be used to study AH incidence.
BACKGROUND: Alcoholic hepatitis (AH) is a clinically diagnosed syndrome with high short-term mortality for which liver transplantation may be curative. A lack of validated algorithms to identify AH hospitalizations has hindered clinical epidemiology research. METHODS: This was a retrospective cohort study of patients with cirrhosis using Veterans Health Administration (VHA) data from 2008 to 2015. We randomly sampled hospitalizations based upon abnormal liver tests and administrative codes for acute hepatitis or alcohol-associated liver disease (ALD). Hospitalizations were manually adjudicated for AH per society guidelines. A priori algorithms were evaluated to compute positive predicted value (PPV) and positive likelihood ratio (LR+), and were tested in an external University of Pennsylvania Health System (UPHS) cohort. RESULTS: Of 368 hospitalizations, 142 (38.6%) were adjudicated as AH. AH patients were younger (55 vs. 58 years, p < 0.001), less likely to have prior cirrhosis decompensation (57% vs. 73.9%, p < 0.001), and had higher AST-to-ALT ratios (median 2.9 vs. 1.9 mg/dL, p < 0.001) and higher bilirubin levels (median 2.9 vs. 1.9 mg/dL, p < 0.001). Algorithms combining clinical laboratory criteria (AST > 85 U/L but < 450 U/L, AST-to-ALT ratio > 2, total bilirubin > 5 mg/dL) and administrative coding criteria yielded the highest PPV (96.4%, 95% CI 87.7-99.6) and the highest LR+ (43.0, 95% CI 10.6-173.5). Several algorithms demonstrated 100% PPV for definite AH in the UPHS external cohort. CONCLUSION: We have identified algorithms for AH hospitalizations with excellent PPV and LR+. These high-specificity algorithms may be used in VHA datasets to identify patients with high likelihood of AH, but should not be used to study AH incidence.
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