The harm-threshold model improves post-transplant survival and ensures fairness for urgent patients, suggesting a new approach.
Background Allocation of scarce donor organs must balance improving overall outcomes with protecting patients at greatest near-term risk. Urgency-focused systems such as MELD (Model for End-Stage Liver Disease) are efficient in saving lives but can reduce total post-transplant survival, whereas unconstrained utility maximization risks bypassing the sickest patients. Methods We propose Harm-Threshold Utilitarianism (HTU), which maximizes expected post-transplant benefit subject to two guardrails: (i) an epistemic threshold requiring sufficient confidence before acting on predicted differences, and (ii) a catastrophic harm threshold that blocks bypassing candidates at high short-term waitlist mortality risk for only marginal or uncertain gains. Using de-identified U.S. liver transplant registry data, we performed proof-of-concept simulations of offer-like pools, comparing HTU with MELD-based selection. Outcomes included Kaplan–Meier curves and 5-year restricted mean survival time (RMST). Sensitivity analyses varied the harm threshold and confidence level. Results HTU transparently reorders candidates relative to MELD while preserving alignment on broad priorities. In simulated pools, HTU-selected recipients achieved higher post-transplant survival; mean RMST improved by approximately 0.25 years per transplant (about three months) at baseline settings. Varying the catastrophic harm threshold produced a clear urgency–efficiency frontier: tighter thresholds selected more urgent patients with smaller gains, while looser thresholds increased gains but allowed more bypasses of urgent candidates. Conclusions HTU operationalizes a tunable, ethically explicit trade-off between benefit and protection of the worst-off. By encoding precaution (confidence threshold) and a non-negotiable floor against catastrophic harm, HTU offers measurable efficiency gains without sacrificing fairness, and reframes policy choices as transparent parameters open to review.
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Yan Lin (2025) studied this question.
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