Construction of a risk prediction model for early-onset peritoneal dialysis-associated peritonitis in peritoneal dialysis patients based on machine learning
10.3760/cma.j.cn115682-20240918-05142
- VernacularTitle:基于机器学习的腹膜透析患者早发性腹膜透析相关性腹膜炎风险预测模型的构建
- Author:
Fang YANG
1
;
Shuwen QIE
;
Li YANG
;
Jianqiu ZHAO
;
Xiaoling BAI
;
Huan LI
Author Information
1. 贵州中医药大学护理学院，贵阳 550000
- Publication Type:Journal Article
- Keywords:
Peritoneal dialysis;
Machine learning;
Peritonitis;
Projection
- From:
Chinese Journal of Modern Nursing
2025;31(6):778-783
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct the risk prediction model for early-onset peritoneal dialysis-associated peritonitis (PDAP) in peritoneal dialysis patients based on six machine learning algorithms.Methods:This study was retrospective. Convenience sampling was used to select peritoneal dialysis patients who were regularly followed up in the Department of Nephrology of Guizhou Provincial People's Hospital from December 2009 to August 2023 to collect general information, primary diseases, and laboratory indicators of the study population. It was randomly divided into a modeling set and validation set in the ratio of 7∶3. With the occurrence of early-onset PDAP as the dependent variable, the risk prediction model of early-onset PDAP in peritoneal dialysis patients was constructed based on six machine learning algorithms, namely, Logistic regression, decision tree, support vector machine, random forest, extreme gradient boosting, and artificial neural network, respectively. Model performance was evaluated based on the area under the receiver operating characteristic curve ( AUC) , accuracy, and F1 score to select the optimal model. Results:The final data of 890 peritoneal dialysis patients were analyzed, of which 86 patients developed early-onset PDAP, and the incidence of early-onset PDAP was 9.66%. The four prediction models, Logistic regression, support vector machine, extreme gradient boosting, and random forest, had high accuracy with AUC values of 0.703, 0.729, 0.782, and 0.814, respectively, with the random forest model having higher AUC value, accuracy, and F1 score. Further ranking of the importance of risk factors for early-onset PDAP based on the random forest model showed that the top five characteristic variables were C-reactive protein, triglycerides, platelet, ferritin, and leukocyte, in that order. Conclusions:The risk prediction model for early-onset PDAP in peritoneal dialysis patients constructed based on the random forest model has optimal performance, which can help medical and nursing staff assess and prevent early-onset PDAP at an early stage.
