Huiyue Hu, Xiaodie Mu, Shuya Zhao, Min Yang, Hua Zhou

Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China

Correspondence: Hua Zhou; Min Yang, Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China, Email zhouhua2323@suda.edu.cn; yangmin1516@suda.edu.cn

Aim: The aim of this study was to develop a predictive model for the progression of diabetic kidney disease (DKD) to end-stage renal disease (ESRD) and to evaluate the effectiveness of renal pathology and the kidney failure risk equation (KFRE) in this context.
Methods: The study comprised two parts. The first part involved 555 patients with clinically diagnosed DKD, while the second part focused on 85 patients with biopsy-proven DKD. Cox regression analysis and competing risk regression were employed to identify independent predictors. Time-dependent receiver operating characteristic (ROC) was used to evaluate prediction performance, and the area under the curve (AUC) was calculated to assess the model’s accuracy.
Results: The Cox regression model developed for the 555 patients clinically diagnosed with DKD identified 5 predictors (body mass index (BMI), estimated glomerular filtration rate (eGFR), 24-hour urinary total protein (UTP), systemic immune-inflammatory index (SII), and controlling nutritional status (CONUT), whereas the Competing risks model included 4 predictors (BMI, eGFR, UTP, CONUT). Among 85 patients with biopsy-proven diabetic DKD, the combined prognostic model integrating KFRE, interstitial fibrosis and tubular atrophy (IFTA), SII and BMI demonstrated enhanced predictive ability at 5 years. The developed models offer improved accuracy over existing methods by incorporating renal pathology and novel inflammatory indices, making them more applicable in clinical settings.
Conclusion: The predictive model proved to be effective in assessing the progression of DKD to ESRD. Additionally, the combined model of KFRE, IFTA, SII, and BMI demonstrates high predictive performance. Future studies should validate these models in larger cohorts and explore their integration into routine clinical practice to enhance personalized risk assessment and management.

Keywords: diabetic kidney disease, end-stage renal disease, pathologies, risk assessment