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

## TL;DR

This study explores how hypothermic machine perfusion parameters affect kidney transplant outcomes and develops a model to predict early graft function risks.

## Contribution

The study introduces a predictive model for early risk stratification in deceased donor kidney transplants using hypothermic machine perfusion parameters.

## Key findings

- 12.9% of deceased donor kidney transplant recipients developed delayed graft function.
- Non-linear relationships were found between hypothermic machine perfusion parameters and delayed graft function risk.
- A predictive model with six variables achieved an AUC of 0.78 in predicting graft function risks.

## Abstract

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.