Evaluating the Effectiveness of OPTN Regulations in Organ Transplantation

Organ transplantation is often the only therapy available to patients with end-stage organ failure. In 2024, over 48,000 transplants were performed in the US, while another 67,000 candidates joined the national waitlist. The Organ Procurement & Transplantation Network (OPTN) manages the national transplant system and oversees the operation of transplant programs (i.e., hospitals that perform transplants). The OPTN has historically monitored transplant program performance using 1-year post-transplant survival metrics. Recently, the OPTN introduced a new set of criteria with additional evaluation metrics to improve performance monitoring. Our study investigates the effectiveness and possible limitations of these regulations using a simulated environment. As a first step, we aim to quantify the prevalence of ‘false flagging’, i.e., the likelihood that a well-performing transplant program is incorrectly flagged and vice-versa. We develop a Monte Carlo simulation framework comprising three phases: (i) preliminary phase, where we estimate patients’ pre- and post-transplant mortality; (ii) simulation phase, where we assign patients to virtual transplant programs and generate simulated patient outcomes; and (iii) evaluation phase, where we assess the accuracy of the new metrics. Because true program-level risks are unobservable, we artificially designate certain programs as high-risk and simulate patient outcomes using preliminary regression models. The models are calibrated using national data on adult first-time kidney transplant candidates waitlisted between 2012 and 2019. Our findings aim to substantiate the OPTN’s efforts to refine performance metrics, prioritize patient safety, and promote increased utilization of deceased-donor organs. KEYWORDS: organ transplantation, Monte Carlo simulation, health policy, data science in healthcare.