Predictive score model for delayed graft function based on easily available variables before kidney donation after cardiac death

Background: How to evaluate the quality of donation after cardiac death (DCD) kidneys has become a critical problem in kidney transplantation in China. Hence, the aim of this study was to develop a simple donor risk score model to evaluate the quality of DCD kidneys before DCD. Methods: A total of 543 qualified kidneys were randomized in a 2:1 manner to create the development and validation cohorts. The donor variables in the development cohort were considered as candidate univariate predictors of delayed graft function (DGF). Multivariate logistic regression was then used to identify independent predictors of DGF with P < 0.05. Date from validation cohort were used to validate the donor scoring model. Results: Based on the odds ratios, eight identified variables were assigned a weighted integer; the sum of the integer was the total risk score for each kidney. The donor risk score, ranging from 0 to 28, demonstrated good discriminative power with a C-statistic of 0.790. Similar results were obtained from validation cohort with C-statistic of 0.783. Based on the obtained frequencies of DGF in relation to different risk scores, we formed four risk categories of increasing severity (scores 0–4, 5–9, 10–14, and 15–28). Conclusions: The scoring model might be a good noninvasive tool for assessing the quality of DCD kidneys before donation and potentially useful for physicians to make optimal decisions about donor organ offers.