Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets

BACKGROUND: 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.