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
Abstract Objective This study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis. Methods A retrospective analysis was conducted on clinical data from 376 patients at Nanhai District People’s...
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BMC
2025-05-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-025-04165-5 |
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| author | Yuehong Wang Zhimin Wu Liuqi Huang Dan Suo Min Zhang Meifen Dai Tianhui You Jing Zheng |
| author_facet | Yuehong Wang Zhimin Wu Liuqi Huang Dan Suo Min Zhang Meifen Dai Tianhui You Jing Zheng |
| author_sort | Yuehong Wang |
| collection | DOAJ |
| description | Abstract Objective This study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis. Methods 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). Results 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 χ 2 = 13.181, P = 0.106; validation set χ 2 = 8.264, P = 0.408). Both DCA and CIC revealed that the nomogram model had good clinical utility in predicting PDAP. Conclusion 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. |
| format | Article |
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| issn | 1471-2369 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Nephrology |
| spelling | doaj-art-b91bf0a2d0d840ebaa2edf1b8e23ebc42025-08-20T01:52:25ZengBMCBMC Nephrology1471-23692025-05-0126111010.1186/s12882-025-04165-5A nomogram for predicting the risk of peritoneal dialysis-associated peritonitis in patients with end-stage renal disease undergoing peritoneal dialysis: model development and validation studyYuehong Wang0Zhimin Wu1Liuqi Huang2Dan Suo3Min Zhang4Meifen Dai5Tianhui You6Jing Zheng7School of Nursing, Guangdong Pharmaceutical UniversityDepartment of Nursing, The Sixth Affiliated Hospital, School of Medicine, South China University of TechnologyDepartment of Nephrology, The Sixth Affiliated Hospital, School of Medicine, South China University of TechnologySchool of Nursing, Guangdong Pharmaceutical UniversitySchool of Public Health, Guangdong Pharmaceutical UniversityDepartment of Nursing, The Sixth Affiliated Hospital, School of Medicine, South China University of TechnologySchool of Continuing Education, Guangdong Pharmaceutical UniversitySchool of Nursing, Guangdong Pharmaceutical UniversityAbstract Objective This study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis. Methods 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). Results 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 χ 2 = 13.181, P = 0.106; validation set χ 2 = 8.264, P = 0.408). Both DCA and CIC revealed that the nomogram model had good clinical utility in predicting PDAP. Conclusion 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.https://doi.org/10.1186/s12882-025-04165-5Dialysis-associated peritonitiscPeritoneal dialysisNomogramClinical prediction modelEnd-stage renal disease |
| spellingShingle | Yuehong Wang Zhimin Wu Liuqi Huang Dan Suo Min Zhang Meifen Dai Tianhui You Jing Zheng 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 BMC Nephrology Dialysis-associated peritonitisc Peritoneal dialysis Nomogram Clinical prediction model End-stage renal disease |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Dialysis-associated peritonitisc Peritoneal dialysis Nomogram Clinical prediction model End-stage renal disease |
| url | https://doi.org/10.1186/s12882-025-04165-5 |
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