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

Liver Transpl. 2021 Jun;27(6):797-807. doi: 10.1002/lt.26002.

Abstract

Patients are prioritized for liver transplantation (LT) under an "urgency-based" system using the Model for End-Stage Liver Disease score. This system focuses solely on waitlist mortality, without considerations of posttransplant morbidity, mortality, and health care use. We sought to develop and internally validate a continuous posttransplant risk score during 5-year and 10-year time horizons. This retrospective cohort study used national registry data of adult deceased donor LT (DDLT) recipients with ≥90 days of pretransplant waiting time from February 27, 2002 to December 31, 2018. We fit Cox regression models at 5 and 10 years to estimate beta coefficients for a risk score using manual variable selection and calculated the absolute predicted survival time. Among 21,103 adult DDLT recipients, 11 variables were selected for the final model. The area under the curves at 5 and 10 years were 0.63 (95% confidence interval [CI], 0.60-0.66) and 0.67 (95% CI, 0.64-0.70), respectively. The group with the highest ("best") scores had 5-year and 10-year survivals of 89.4% and 85.4%, respectively, compared with 45.9% and 22.2% for those with the lowest ("worst") scores. Our score was significantly better at predicting long-term survival compared with the existing scores. We developed and validated a risk score using nearly 17 years of data to prioritize patients with end-stage liver disease based on projected posttransplant survival. This score can serve as the building block by which the transplant field can change the entire approach to prioritizing patients to an approach that is based on considerations of maximizing benefits (ie, survival benefit-based allocation) rather than simply waitlist mortality.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • End Stage Liver Disease* / surgery
  • Humans
  • Liver Transplantation* / adverse effects
  • Retrospective Studies
  • Severity of Illness Index
  • Waiting Lists