A model for calculating the long-term estimated post-transplant survival of deceased donor liver transplant patients.

Background: The estimated long-term survival (EPTS) score is used for kidney allocation. A comparable prognostic tool to accurately quantify EPTS benefit in deceased donor liver transplant (DDLT) candidates is nonexistent.
Methods: Using the Scientific Registry of Transplant Recipients (SRTR) database, we developed, calibrated, and validated a nonlinear regression equation to calculate liver-EPTS (L-EPTS) for 5- and 10-year outcomes in adult DDLT recipients. The population was randomly split (70:30) into two discovery (N = 26,372 and N = 46,329) and validation cohorts (N = 11,288 and N = 19,859) for 5- and 10-year post-transplant outcomes, respectively. Discovery cohorts were used for variable selection, Cox proportional hazard regression modeling, and nonlinear curve fitting. Eight clinical variables were selected to construct the L-EPTS formula, and a five-tiered ranking system was created.
Findings: Tier thresholds were defined and the L-EPTS model was calibrated (R ²  = 0.96 [5-year] and 0.99 [10-year]). Patients' median survival probabilities in the discovery cohorts for 5- and 10-year outcomes ranged from 27.94% to 89.22% and 16.27% to 87.97%, respectively. The L-EPTS model was validated via calculation of receiver operating characteristic (ROC) curves using validation cohorts. Area under the ROC curve was 82.4% (5-year) and 86.5% (10-year).
Interpretation: L-EPTS has high applicability and clinical utility because it uses easily obtained pre-transplant patients characteristics to accurately discriminate between those who are likely to receive a prolonged survival benefit and those who are not. It is important to evaluate medical urgency alongside survival benefit and placement efficiency when considering the allocation of a scarce resource.
Funding: There are no funding sources related to this project.
Competing Interests: Declaration of interests No funding was used to support this work. The authors of this manuscript have no conflicts of interest to disclose as described by eBioMedicine.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)