Development and Validation of a Model to Predict Long‐Term Survival After Liver Transplantation

BACKGROUND: Patients are prioritized for liver transplant under an ‘urgency-based’ system using the Model for End Stage Liver Disease score. This system focuses solely on waitlist mortality, without considerations of post-transplant morbidity, mortality, and healthcare utilization. We sought to develop and internally validate a continuous post-transplant risk score over 5- and 10-year time horizons. METHODS: This retrospective cohort study used national registry data of adult deceased-donor liver transplant (DDLT) recipients with ≥90 days of pre-transplant waiting time from 2/27/02–12/31/18. We fit Cox regression models at 5 and 10 years to estimate beta coefficients for a risk score using manual variable selection and calculated absolute predicted survival time. RESULTS: Among 21,103 adult DDLT recipients, 11 variables were selected for the final model. The AUC’s at 5- and 10-years were: 0.63, 95% CI: 0.60–0.66 and 0.67, 95% CI: 0.64–0.70, respectively. The group with the highest (‘best’) scores had 5- and 10-year survivals of 89.4% and 85.4%, respectively, compared to 45.9% and 22.2% for those with the lowest (‘worst’) scores. Our score was significantly better at predicting long-term survival compared to existing scores. CONCLUSION: We developed and validated a risk score using nearly 17 years of data to prioritize patients with end-stage liver disease based on projected post-transplant survival. This score can serve as the building block by which the transplant field can change the entire approach to prioritizing patients to one that is based on considerations of maximizing benefits (i.e., survival benefit-based allocation) rather than simply waitlist mortality