Abstract
The scarcity of organ donors relative to the number of patients with End Stage Kidney Disease (ESKD) has led to prolonged waiting times for kidney transplants, contributing to elevated cardiovascular mortality risk. Transplant professionals are tasked with the complex allocation of limited organs to a vulnerable patient group facing heightened morbidity and mortality risk. The need for continuous re-evaluation of waitlisted patients is evident due to the significant number who perish while awaiting transplantation. Among individuals with ESKD, vascular calcification, particularly Abdominal Arterial Calcifications (AAC), holds predictive value for cardiovascular events and mortality. However, a standardized method to quantify AAC's prognostic potential remains lacking, especially for kidney transplant evaluations. This study presents an ensemble machine learning (ML) approach to study the relationship between AAC score and mortality in patients on the waitlist and triage patients needing transplantation. Using the AAC score, the proposed ML model can predict kidney transplant waitlist morality with an accuracy of 78% while its accuracy is 68% without using this score. This study leverages explainable ML to explore the relationship between predictors and mortality in waitlisted patients, aiming to improve patient triage accuracy.