Nomogram Prediction Modeling of Peritoneal Dialysis-Associated Peritonitis

Abstract

In order to find the risk factors of Peritoneal Dialysis-Associated Peritonitis (PDAP) and to construct a nomogram model for predicting the risks of PDAP, providing a basis for the prevention and treatment of PDAP. We collected the data of 181 peritoneal dialysis (PD) patients admitted to the Nephrology Department of Jiangsu Provincial Hospital of Traditional Chinese Medicine from February 2021 to October 2023. We divided them into the peritonitis group (n=62) and the non-peritonitis group (n=119) according to the hospital infection. Logistic regression analysis was used for risk factor screening, and variables with statistically significant differences in univariate analysis were included in a multifactorial Logistic regression analysis model to explore the influencing factors of Peritoneal Dialysis-Associated Peritonitis and to construct a nomogram prediction model, and by plotting the subjects' work characteristics (ROC) curve, Hosmer-Lemeshow calibration curve, and clinical decision curve (DCA) to evaluate the differentiation, calibration, and clinical applicability of the model. Resultly, based on multifactorial logistic regression analysis incorporating body mass index(BMI), age at peritoneal dialysis, neutrophil percentage/lymphocyte percentage, albumin, blood potassium, blood uric acid, blood phosphorus, and Chinese medicine concurrently were the risk factors for PDAP (P<0.05). Based on the constructed nomogram model, the ROC curve showed that the area under the curve (AUC) of the model was 0.959. Additionally, the calibration curve and DCA curve demonstrated good accuracy and application value. In conclusion, the nomogram prediction model constructed in this study has good predictive efficacy for PDAP, which can provide a reference for the early detection and identification of PDAP by clinicians and nurses.