ArticleBMC nephrology2025
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.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development and validation of a nomogram for peritoneal dialysis-associated peritonitis.Renal failure · 2026Article
- Application of targeted next-generation sequencing in the etiological diagnosis of peritoneal dialysis-associated peritonitis: a single-center prospective cohort study.International urology and nephrology · 2026Article
- Development and validation of a nomogram for early prognostic prediction in childhood aplastic anemia receiving cyclosporine monotherapy.Pediatric research · 2026Article
- Development and validation of a nomogram for predicting treatment failure in culture-negative peritoneal dialysis-associated peritonitis.Clinical kidney journal · 2026Article
- Neutrophil-to-lymphocyte ratio as a harbinger of peritonitis in peritoneal dialysis: a case-control study.Frontiers in medicine · 2026Article
- Prediction of Imminent Peritoneal Dialysis-Associated Peritonitis Using Time-Updated Electronic Health Records and Machine Learning: A Temporal Validation Study.Journal of inflammation research · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis.
methodsA 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).
resultsEight potential predictors were selected by LASSO regression analysis. Multivariate logistic regression analysis confirmed that age, dialysis duration, albumin, hemoglobin, β
conclusionThe 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.
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