Abstract: Objective To analyze the risk factors for rapid decline of residual renal function (RRF) in peritoneal dialysis (PD) patients and to establish a predictive model. Methods PD patients who visited the Department of Nephrology at the Second Xiangya Hospital of Central South University from January 1, 2012, to December 31, 2022, were enrolled. Based on the median rate of RRF decline, patients were divided into a rapid decline group and a slow decline group. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for preliminary variable selection. Independent risk factors associated with rapid RRF decline were then determined via multivariable stepwise logistic regression analysis. A nomogram prediction model was constructed based on the final predictors. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, the Hosmer-Lemeshow test, and decision curve analysis (DCA). Internal validation of the model was performed using the Bootstrap method. Results A total of 710 patients were included, with 354 in the rapid decline group and 356 in the slow decline group. LASSO regression and multivariable stepwise logistic regression identified that body mass index (BMI)＞28 (OR=2.640，95% CI：1.180～5.926，P=0.018), baseline RRF≥2(OR=3.140，95%CI：1.845～5.342，P＜0.001), and serum magnesium (OR=0.341，95%CI：0.128～0.909，P=0.032) as independent risk factors for rapid RRF decline in PD patients. The nomogram prediction model constructed based on these factors exhibited an  AUC of 0.719 (95%CI：0.682～0.756), and internal validation via 1000 Bootstrap resamples yielded an AUC of 0.722 (95%CI：0.685～0.759).The Hosmer-Lemeshow goodness-of-fit test yielded χ²=5.268 (P=0.729), and DCA indicated clinical applicability across a threshold probability range of 0.01～0.91. Conclusion    BMI, baseline RRF, and serum magnesium are independent risk factors for rapid RRF decline in PD patients. The nomogram prediction model based on these factors demonstrates good discrimination, calibration, and clinical applicability.

Key words: Peritoneal dialysis, Residual renal function decline rate, Risk factors