Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis

Abstract

Objective To identify the risk factors of refractory peritoneal dialysis related peritonitis (PDRP) and construct a nomogram to predict the occurrence of refractory PDRP. Methods Refractory peritonitis was defined as the peritonitis episode with persistently cloudy bags or persistent dialysis effluent leukocyte count >100 × 10 9 /L after 5 days of appropriate antibiotic therapy. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, LASSO regression analysis, and random forest algorithms were utilized to identify the potential risk factors for refractory peritonitis. Independent risk factors identified using multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, Hosmer–Lemeshow test, calibration curve, and decision curve analysis. Results A total of 294 peritonitis episodes in 178 patients treated with peritoneal dialysis (PD) were enrolled, of which 93 were refractory peritonitis. C-reactive protein, serum albumin, diabetes mellitus, PD duration, and type of causative organisms were independent risk factors for refractory peritonitis. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.781 (95% CI: 0.716–0.847) in the training set and 0.741 (95% CI: 0.627–0.855) in the validation set. The Hosmer–Lemeshow test and calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting refractory peritonitis. Conclusion This nomogram can accurately predict refractory peritonitis in patients treated with PD.