Mohsen ALIAKBARIAN, Vahid GHAVAMI, Fahimeh HOSEINZADEH, Rozita KHODASHAHI
Experimental and Clinical Transplantation - 2026;24(3):260-267
Objectives: For liver transplant candidates, transplantation or death on wait lists can be competing risks. This study used competing risk analysis to estimate the probability of death among patients on wait lists for liver transplant. Materials and Methods: We retrospectively analyzed liver transplant candidates registered at the Montaseriyeh Transplant Center Registry of Mashhad University of Medical Sciences (Iran) from 2013 to 2024. We followed patients from listing through transplant, death, or end of study. We collected demographic, clinical, laboratory, and follow-up details. We used Gray's test to assess cumulative incidence of death across different listing periods, orthotopic liver transplant groups, and Model for End-Stage Liver Disease severity levels. To estimate the effects of various covariates while accounting for transplantation as a competing event, we conducted a competing risk regression using the Fine and Gray subdistribution hazard model. We used R software (cmprsk and cuminc packages) for statistical analyses. Results: Average age of patients was 50.84 +/- 13.78 years. Over the follow-up period, 503 patients (60.0%) received transplants, 233 (27.8%) died while waiting for transplant, and 102 (12.2%) were administratively censored. Among transplant patients, 65.9% had Model for End-Stage Liver Disease scores between 10 and 20, with mortality increasing with increased scores. The hazard model showed no significant differences in death risk by age, sex, marital status, year of transplant, or etiology group. However, patients with higher Model for End-Stage Liver Disease scores had significantly greater risk of death than those with lower scores (P < .001). Conclusions: Increased Model for End-Stage Liver Disease score emerged as the most significant predictor of mortality among patients waiting for liver transplant. Focusing on candidates with high scores and tackling socioeconomic barriers could improve survival outcomes. These insights can inform future approaches to optimize patient prioritization and transplant allocation.
