Multivariate logistic analysis of end-stage renal disease in diabetic nephropathy and construction of individualized nomogram prediction model

BACKGROUND: Diabetic kidney disease (DKD) is a major contributor to end-stage renal disease (ESRD) in diabetic patients. Early identification of high-risk individuals and predictive modeling can aid in improving outcomes. The development of a practical nomogram model for ESRD risk stratification is critical for guiding clinical decision-making in DKD populations. MATERIALS AND METHODS: This retrospective single-center study included 250 patients diagnosed with diabetic nephropathy (DN) between January 2017 and October 2019. Patients were followed for 5 years and classified into ESRD and non-ESRD groups. Baseline variables were analyzed using univariate and multivariate logistic regression to identify independent risk factors. A nomogram was developed based on key predictors. Internal validation was performed via the bootstrap method (1,000 resamples), and external validation used an independent cohort of 85 DN patients from January to June 2020. The nomogram was designed to provide clinicians with a visual tool for individualized risk assessment. RESULTS: Of the 230 eligible patients, 78 (33.91%) developed ESRD. Systolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), serum creatinine (Scr), estimated glomerular filtration rate (eGFR), and urine protein-to-creatinine ratio (UPCR) were identified as independent predictors (p < 0.05). The nomogram incorporating these variables showed strong performance: internal AUC = 0.836 (95% CI: 0.787 - 0.886); external AUC = 0.828 (95% CI: 0.775 - 0.881). Calibration curves and Hosmer-Lemeshow goodness-of-fit test (HLGFT) confirmed good model fit and predictive accuracy. The model's simplicity and discriminative power make it suitable for routine clinical use. CONCLUSION: The nomogram model based on six baseline variables demonstrates high predictive value for ESRD progression in DKD patients and may guide early risk assessment and personalized management. This tool enables clinicians to designate high-risk patients efficiently, facilitating timely interventions to delay ESRD onset.