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

Abstract

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.

Keywords:

• cirrhosis
• phenotyping
• administrative data
• claims

Data Sharing Statement

No additional data are available.

Ethics

The Brigham and Women’s Hospital Institutional Review Board approved this study protocol. All accessed data complied with relevant data protection and privacy regulations.

Disclosure

TGS has received research funding from Amgen and has received consulting fees from Aetion, for work unrelated to this manuscript. SS is participating in investigator-initiated grants to the Brigham and Women’s Hospital from Boehringer Ingelheim unrelated to the topic of this study. He is a consultant to Aetion Inc., a software manufacturer of which he owns equity. His interests were declared, reviewed, and approved by the Brigham and Women’s Hospital and Partners HealthCare System in accordance with their institutional compliance policies. LGB reports personal fees from Amazon Web Services, personal fees from Aetion Inc., outside the submitted work. The authors report no other conflicts of interest in this work.

Additional information

Funding

NIH RO1LM013204 (Lin), NIH K23 DK122104 (Simon), NIH R01HL167021 (Simon). No funding organization had any role in the design and conduct of the study; in the collection, management, and analysis of the data; or in the preparation, review, and approval of the manuscript.