A High-Precision Machine Learning-Based Prediction Model for Delayed Graft functon(DGF) in Chinese Kidney Transplant Patients: A Multicenter Study

Delayed graft function (DGF) is a severe complication following kidney transplantation, and currently, there is a lack of accurate prediction tools tailored for the Chinese population. This study integrates data from 1,093 kidney transplant cases across four medical centers in China (2016–2024) to develop and validate a machine learning-based model for DGF prediction. By comparing nine machine learning algorithms, we found that the LightGBM model performed best in external validation (AUC = 0.80, accuracy = 0.73). SHAP analysis identified donor GFR, donor hemoglobin, and recipient plasma BNP levels as the primary predictive factors, while also highlighting novel predictors such as donor microscopic hematuria and APTT. Cox regression analysis showed that preoperative dialysis duration in recipients (HR = 1.006, 95% CI: 1.001–1.012) was an independent predictor of DGF recovery. In the follow-up study, we observed that while the DGF mortality group exhibited the most significant kidney function impairment (serum creatinine β = 200.57, eGFR β = -39.91), the prognosis of the DGF survival group was comparable to that of the non-DGF survival group. Additionally, the duration of DGF (16.66 ± 13.73 vs. 15.44 ± 14.62 days) and the number of dialysis treatments (8.13 ± 7.39 vs. 7.78 ± 7.22 sessions) were not significantly associated with prognosis. Based on these findings, we developed an online prediction platform (www.kidney-dgf-match.cn) to support clinical decision-making. This study not only establishes the first high-precision DGF prediction model for the Chinese population but also reveals the potential for favorable outcomes in DGF patients with proper management, offering new insights for optimizing post-transplant management strategies.