Association between hypothermic machine perfusion parameters and graft function in deceased donor kidney transplantation

Kidney transplantation (KT) is the most effective treatment for end-stage renal disease. Hypothermic machine perfusion (HMP) can improve renal energy metabolism and reduce ischemia-reperfusion injury compared with static cold storage. This study aimed to evaluate the association between HMP parameters and graft function in deceased donor kidney transplantation (DDKT) and to develop a predictive model for early risk stratification. A retrospective analysis was conducted on 2,041 DDKT recipients from 1 January 2015 to 30 June 2023. The primary outcome, delayed graft function (DGF), was defined as the need for at least one dialysis session within the first week after transplantation. Consensus clustering (CC) and restricted cubic spline (RCS) analysis were used to evaluate the associations between clinical data, HMP parameters, and graft function. Feature selection was performed using Lasso-penalized logistic regression (LR), and multivariable LR was used to construct the predictive model. The model’s performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Among the DDKT recipients, 12.9% developed DGF. HMP parameters varied significantly between the two groups, with DGF recipients showing distinct patterns in perfusion resistance, flux, and pressure. CC identified two recipient clusters with distinct DGF risk profiles, graft function, and donor characteristics. Non-linear relationships were identified between HMP parameters and DGF risk, with thresholds for initial resistance, terminal resistance, and terminal flux. The predictive model integrating six variables achieved an AUC of 0.78 (95% CI: 0.76–0.82) in the test set. Calibration and DCA confirmed good reliability and net clinical benefit. Non-linear relationships between HMP parameters and DGF underscore graft perfusion complexity. The proposed model demonstrated robust internal performance and may support early post-transplant risk stratification. External validation in independent cohorts is warranted to confirm generalizability and clinical applicability.肾移植（Kidney transplantation, KT）是终末期肾病（end-stage renal disease）的最优治疗手段。相较于静态冷储存，低温机械灌注（Hypothermic machine perfusion, HMP）可改善肾脏能量代谢，减轻缺血再灌注损伤（ischemia-reperfusion injury）。本研究旨在探讨尸体供肾肾移植（deceased donor kidney transplantation, DDKT）中低温机械灌注参数与移植物功能的关联，并构建早期风险分层预测模型。研究纳入2015年1月1日至2023年6月30日的2041例DDKT受者进行回顾性分析。本研究的主要结局为移植肾功能延迟恢复（delayed graft function, DGF），定义为移植术后1周内需至少1次透析治疗。采用共识聚类（Consensus clustering, CC）与限制性立方样条（restricted cubic spline, RCS）分析，评估临床资料、HMP参数与移植物功能的相关性；通过Lasso惩罚logistic回归（Lasso-penalized logistic regression, LR）进行特征筛选，并采用多变量logistic回归构建预测模型。采用受试者工作特征曲线下面积（area under the curve, AUC）、校准曲线及决策曲线分析（decision curve analysis, DCA）评估模型性能。结果显示，纳入的DDKT受者中12.9%发生了DGF；两组受者的HMP参数存在显著差异，发生DGF的受者在灌注阻力、流量及压力层面呈现独特的变化模式。共识聚类识别出两个具有不同DGF风险分层、移植物功能及供者特征的受者亚群。研究发现HMP参数与DGF风险间存在非线性关联，并确定了初始阻力、终末阻力及终末流量的阈值。整合6个变量的预测模型在测试集上的AUC为0.78（95%置信区间：0.76~0.82），校准曲线与决策曲线分析证实该模型具备良好的可靠性与净临床获益。HMP参数与DGF之间的非线性关联凸显了移植物灌注过程的复杂性。本研究构建的模型展现出稳健的内部验证性能，可用于移植术后早期风险分层；未来需在独立队列中开展外部验证，以确认其泛化能力与临床应用价值。