Predictive Score Model for Delayed Graft Function Based on Hypothermic Machine Perfusion Variables in Kidney...

Area of Science:

• Nephrology
• Transplantation Surgery
• Medical Devices

Background:

• Hypothermic machine perfusion (HMP) is increasingly used for kidney transplantation after cardiac death (DCD).
• Predicting delayed graft function (DGF) using HMP parameters remains controversial.
• A need exists for reliable methods to assess DCD kidney quality and predict DGF.

Purpose of the Study:

• To develop and validate a scoring model using readily available HMP variables to predict DGF in DCD kidney transplantation.
• To identify high-risk DCD kidneys for DGF.
• To provide guidance for organ allocation and assessment of DCD kidneys.

Main Methods:

• A development cohort (n=244) and a validation cohort (n=122) were established from 366 kidneys undergoing HMP.
• Univariate and multivariate logistic regression identified independent predictors of DGF from HMP variables.
• A weighted scoring model was created based on odds ratios, and its accuracy was validated.

Main Results:

• HMP duration, resistance, and flow rate were identified as independent predictors of DGF.
• The developed HMP predictive score (0-14) showed a clear correlation with DGF incidence across four risk categories.
• The HMP predictive score demonstrated good discriminative power (c-statistic=0.706) and superior prediction of DGF compared to terminal flow and resistance alone.

Conclusions:

• The HMP predictive score serves as a valuable noninvasive tool for assessing DCD kidney quality.
• This scoring system can assist physicians in making informed decisions regarding the allocation of donated organs.
• The model offers improved prediction of DGF compared to existing methods.