ArticleBMC nephrology2025
Risk prediction of cardiovascular events in peritoneal dialysis patients.
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 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.BMC nephrology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular events (CVEs), which refer to a spectrum of conditions including heart attacks, stroke and peripheral vascular disease, are the primary cause of death among peritoneal dialysis (PD) patients, accounting for nearly 40% of deaths. Early identification of high-risk individuals is essential to lessen this burden. Machine learning is particularly suited for this task due to its ability to discern complex, non-linear relationships between various clinical variables, which is essential for accurately predicting CVEs in the context of PD. Our study aimed to develop a predictive machine learning model to identify PD patients at risk of CVEs, offering healthcare providers a tool for proactive intervention.
methodsA total of 251 PD patients were enrolled in the study, with an additional 42 patients included for external validation. Initially, 37 variables were collected but reduced to 25 via Lasso regression. Six supervised machine learning algorithms were evaluated, and XGBoost was chosen as the optimal model based on AUC. Both internal and external validation confirmed the model's efficacy, and a web application was developed using the final XGBoost model, which utilized 12 selected variables.
resultsAmong the 251 patients, 40 (15.94%) developed CVEs. The XGBoost model demonstrated an AUC of 0.94 in 5-fold cross-validation. A simplified XGBoost model using 12 variables demonstrated robust prediction capabilities with an AUC of 0.88 in 5-fold cross-validation and 0.78 in external validation. The top five predictors of CVEs were age at catheterization, height, HDL, gender and hemoglobin. According to the SHAP summary plot, older age at catheterization, shorter height, male gender, higher serum HDL and lower hemoglobin levels correlated with increased CVEs risk in PD patients.
conclusionsThe machine learning model, based on 12 key variables, offers an effective tool for predicting CVEs in PD patients, enabling early identification of high-risk cases. This model has been integrated into a web application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.