Clinical Epidemiology (Mar 2023)
Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets
Abstract
Tracey G Simon,1,2 Sebastian Schneeweiss,2 Richard Wyss,2 Zhigang Lu,2 Lily G Bessette,2 Cassandra York,2 Kueiyu Joshua Lin1,2 1Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 2Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Kueiyu Joshua Lin, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont St. Suite 3030, Boston, MA, 02120, USA, Tel +1 617 278-0930, Fax +1 617 232-8602, Email [email protected]: The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR).Methods: We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007– 2017), and a Medicaid-linked EHR network (external validation; 2000– 2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD< 10, MELD≥ 15 and MELD≥ 20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance.Results: We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD< 10 and MELD≥ 15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥ 20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD< 10 (95% CI=0.75– 0.78), 85% of patients with MELD≥ 15 (95% CI=0.84– 0.87), and 87% of patients with MELD≥ 20 (95% CI=0.86– 0.88). Results were consistent in the external validation set (n=2240).Conclusion: Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.Keywords: cirrhosis, phenotyping, administrative data, claims
