Karen Y Xiao1, Rebecca A Hubbard2, David E Kaplan3,4, Tamar H Taddei5,6, David S Goldberg7, Nadim Mahmud8,9. 1. Department of Internal Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St, 100 Centrex, Philadelphia, PA, 19104, USA. 2. Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 604 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA. 3. Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, PCAM 7S GI, 4th Floor, South Pavilion, Philadelphia, PA, 19104, USA. 4. Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA. 5. Division of Digestive Diseases, Yale University School of Medicine, Digestive Diseases, 333 Cedar Street, PO Box 208019, New Haven, CT, 06520, USA. 6. VA Connecticut Healthcare System, West Haven, CT, USA. 7. Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, 1120 NW 14th St, Suite 1112 (D49), Miami, FL, 33136, USA. 8. Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, PCAM 7S GI, 4th Floor, South Pavilion, Philadelphia, PA, 19104, USA. mahmudn@pennmedicine.upenn.edu. 9. Leonard David Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA. mahmudn@pennmedicine.upenn.edu.
Abstract
BACKGROUND AND PURPOSE: The diagnosis of acute on chronic liver failure (ACLF) carries a high short-term mortality, making early identification of at-risk patients crucial. To date, there are no models that predict which patients with compensated cirrhosis will develop ACLF, and limited models exist to predict ACLF mortality. We sought to create novel risk prediction models using a large North American cohort. METHODS: We performed a retrospective study of 75,922 patients with compensated cirrhosis from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) dataset. Using 70% derivation/30% validation sets, we identified ACLF patients using the Asian Pacific Association of Liver (APASL) definition. Multivariable logistic regression was used to derive prediction models (called VOCAL-Penn) for developing ACLF at 3, 6, and 12 months. We then created prediction models for ACLF mortality at 28 and 90 days. RESULTS: The VOCAL-Penn models for ACLF development had very good discrimination [concordance (C) statistics of 0.93, 0.92, and 0.89 at 3, 6, and 12 months, respectively] and calibration. The mortality models also had good discrimination at 28 and 90 days (C statistics 0.89 and 0.88, respectively), outperforming the Model for End-stage Liver Disease (MELD), MELD-sodium, and the APASL ACLF Research Consortium ACLF scores. CONCLUSION: We have developed novel tools for predicting development of ACLF in compensated cirrhosis patients, as well as for ACLF mortality. These tools may be used to proactively guide patient follow-up, prognostication, escalation of care, and transplant evaluation. Receiver operating characteristic (ROC) curves for predicting development of APASL ACLF at 3 months (a), 6 months (b), and 1 year (c).
RCT Entities:
BACKGROUND AND PURPOSE: The diagnosis of acute on chronic liver failure (ACLF) carries a high short-term mortality, making early identification of at-risk patients crucial. To date, there are no models that predict which patients with compensated cirrhosis will develop ACLF, and limited models exist to predict ACLF mortality. We sought to create novel risk prediction models using a large North American cohort. METHODS: We performed a retrospective study of 75,922 patients with compensated cirrhosis from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) dataset. Using 70% derivation/30% validation sets, we identified ACLF patients using the Asian Pacific Association of Liver (APASL) definition. Multivariable logistic regression was used to derive prediction models (called VOCAL-Penn) for developing ACLF at 3, 6, and 12 months. We then created prediction models for ACLF mortality at 28 and 90 days. RESULTS: The VOCAL-Penn models for ACLF development had very good discrimination [concordance (C) statistics of 0.93, 0.92, and 0.89 at 3, 6, and 12 months, respectively] and calibration. The mortality models also had good discrimination at 28 and 90 days (C statistics 0.89 and 0.88, respectively), outperforming the Model for End-stage Liver Disease (MELD), MELD-sodium, and the APASL ACLF Research Consortium ACLF scores. CONCLUSION: We have developed novel tools for predicting development of ACLF in compensated cirrhosispatients, as well as for ACLF mortality. These tools may be used to proactively guide patient follow-up, prognostication, escalation of care, and transplant evaluation. Receiver operating characteristic (ROC) curves for predicting development of APASL ACLF at 3 months (a), 6 months (b), and 1 year (c).
Entities:
Keywords:
AARC-ACLF; Acute on chronic liver failure; Asia pacific association of the study of the liver; Cirrhosis; Liver transplant; Mortality model; Outcomes model; Prediction model; Veterans health; Veterans outcomes and costs associated with liver disease
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