ArticleScientific reports2018
Development and validation of risk prediction models for cardiovascular mortality in Chinese people initialising peritoneal dialysis: a cohort study.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Developing an explainable machine learning model using body composition to predict cardiovascular mortality in initial dialysis patients: a multicenter study.Frontiers in physiology · 2026Article
- Triglyceride-glucose index predicts the mortality risk among incident peritoneal dialysis patients in a cohort study.Scientific reports · 2025Observational
- Risk factors for cardiovascular death in urgent-start peritoneal dialysis patients: a multicenter retrospective cohort study.Scientific reports · 2025Article
- Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study.Renal failure · 2024Article
- Triglyceride glucose-body mass index and cardiovascular mortality in patients undergoing peritoneal dialysis: a retrospective cohort study.Lipids in health and disease · 2023Article
- The significance of follow-up serum uric acid levels in predicting all-cause mortality and cardiovascular mortality in peritoneal dialysis patients.Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2023Article
- Prediction model for cardiovascular disease risk in hemodialysis patients.International urology and nephrology · 2022Article
- Association of the triglyceride-glucose index and vascular target organ damage in a Beijing community-based population.Frontiers in cardiovascular medicine · 2022Article
- Platelet-to-albumin ratio: a risk factor associated with technique failure and mortality in peritoneal dialysis patients.Renal failure · 2021Article
- Serum C-reactive protein to albumin ratio and mortality associated with peritoneal dialysis.Renal failure · 2020Article
- Observational
- Observational
- Observational
- Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis.PloS one · 2019Article
- Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study.BMJ open diabetes research & care · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease is the leading cause of death among patients receiving peritoneal dialysis. We aimed to develop and validate a risk prediction model for cardiovascular death within 2 years after the initiation of peritoneal dialysis (PD). A cohort including all patients registered with the Henan Peritoneal Dialysis Registry (HPDR) between 2007 and 2014. Multivariate logistic regression analysis was used to develop the risk prediction model. The HPDR data was randomly divided into two cohorts with 60% (1,835 patients) for model derivation, and 40% (1,219 patients) for model validation. The absolute rate of cardiovascular mortality was 14.2% and 14.4 in the derivation and validation cohort, respectively. Age, body mass index, blood pressure, serum lipids, fasting glucose, sodium, albumin, total protein, and phosphorus were the strongest predictors of cardiovascular mortality in the final model. Discrimination of the model was similar in both cohorts, with a C statistic above 0.70, with good calibration of observed and predicted risks. The new prediction model that has been developed and validated with clinical measurements that are available at the point of initiation of PD and could serve as a tool to screen for patients at high risk of cardiovascular death and tailor more intensive cardio-protective care.
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