A nomogram for predicting the risk of peritoneal dialysis-associated peritonitis in patients with end-stage renal disease undergoing peritoneal dialysis: model development and validation study

This study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis. A retrospective analysis was conducted on clinical data from 376 patients at Nanhai District People's Hospital in Foshan City, Guangdong Province, between December 2017 and December 2024. The dataset was randomly divided into a training set (n = 244) and a validation set (n = 132). Risk factors for PDAP were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression, and a predictive nomogram was developed and validated using R4.1.3. The model's performance was evaluated through receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, decision curve analysis (DCA), and clinical impact curves (CICs). Eight potential predictors were selected by LASSO regression analysis. Multivariate logistic regression analysis confirmed that age, dialysis duration, albumin, hemoglobin, β2-microglobulin, Potassium and lymphocyte count were independent risk factors for PDAP occurrence (P = 0.001). The nomogram's area under the curve (AUC) was 0.929 (95% CI: 0.896-0.962) in the training set and 0.905 (95% CI: 0.855-0.955) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated a good model fit (training set χ2 = 13.181, P = 0.106; validation set χ2 = 8.264, P = 0.408). Both DCA and CIC revealed that the nomogram model had good clinical utility in predicting PDAP. The proposed nomogram exhibited excellent predictive performance and clinical utility, providing a valuable tool for early identification and intervention in PDAP. Further external validation and prospective studies are recommended.