Early Refusal Pattern Phenotyping as a Surrogate for Organ Quality Assessment in Kidney Allocation

To the Editor: We read with interest the recent article by Guan et al,1 which provides a comprehensive and methodologically thoughtful assessment of refusal behavior in deceased donor kidney allocation. Their distinction between single-patient and multiple-patient simultaneous refusals, derived through timestamp-based clustering, offers a methodologically robust framework that elevates routine offer-response data into a meaningful surrogate for real-time assessment of organ suitability. This approach is particularly valuable in allocation environments where decisions must be made under substantial time pressure and with incomplete ancillary information. A notable feature of the study lies in the clear behavioral divergence identified between the 2 refusal categories. Organ-level concerns—including biopsy abnormalities, preservation injury, prolonged ischemia, or suspected malignancy—were almost exclusively concentrated within multiple-patient refusals, whereas single-patient refusals reflected candidate-specific issues such as crossmatch incompatibility or temporary ineligibility.1 This pattern supports the authors’ conceptual model and reinforces the notion that coordinated refusal patterns encode tacit expert judgment. Furthermore, the steep gradient in nonutilization probability associated with increasing numbers of multiple-patient refusals—culminating in an 88% likelihood of discard when 5 or more such refusals occurred—highlights the strong predictive fidelity of refusal clustering.1 Notably, organs accumulating several single-patient refusals exhibited markedly lower discard risk, underscoring the distinction between candidate-driven and organ-driven signals. These findings carry relevance for India, where deceased donor transplantation remains limited despite substantial national efforts to expand donation networks and strengthen allocation infrastructure.2 Indian programs operate with marked heterogeneity in retrieval practices, logistics, and access to rapid biopsy or perfusion data, making early behavioral indicators especially useful. The integration of refusal-pattern analytics could support current allocation workflows by enabling earlier identification of organs at risk of discard and facilitating accelerated placement before cold ischemia becomes prohibitive.2 In a setting where living donation predominates, optimizing utilization through behavior-informed decision support could yield significant clinical impact. A brief, context-appropriate “refusal-cluster alert” could also be incorporated into India’s allocation platforms. An automated flag triggered when ≥3 simultaneous or near simultaneous refusals cite organ-related concerns—such as suboptimal biopsy findings, prolonged ischemia, or structural abnormalities—may prompt earlier reassessment or accelerated placement. This low-complexity mechanism could reduce avoidable delays and help minimize discard in resource-variable settings.1,3 The study’s insights also intersect with evolving Indian immunological practice. Early identification of organs repeatedly declined for organ-related reasons may guide centers toward more rigorous immunologic evaluation or amplify caution in high-risk donor-recipient combinations, aligning with consensus priorities for individualized, risk-adapted transplantation.3 Nonetheless, important challenges remain. Certain refusal-reason codes, including nonspecific logistical categories or ambiguous size descriptors, displayed inconsistent alignment with the proposed behavioral taxonomy, suggesting that refinement of refusal coding could further enhance predictive accuracy.1 Furthermore, generalizing these findings to diverse global systems requires careful attention to variation in donor characteristics, allocation algorithms, and center-level thresholds for organ acceptance. In summary, Guan et al1 provide a thoughtful and impactful analysis that advances the interpretive utility of refusal-pattern data. Their observations offer global relevance and hold potential for strengthening allocation efficiency in India’s evolving deceased donor landscape.