ArticlePloS one2017
Development of a new risk model for predicting cardiovascular events among hemodialysis patients: Population-based hemodialysis patients from the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS).
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Systematic review and meta-analysis of cardiovascular event risk prediction models in maintenance hemodialysis patients.Scientific reports · 2025Pooled it
- I Brazilian guideline on hypertension in dialysis of the Brazilian Society of Nephrology.Jornal brasileiro de nefrologiaGuideline
- Associations of Mean Corpuscular Hemoglobin with Survival and Cardiovascular Outcomes Among Patients with Hemodialysis in Japan Based on Real-World Data.Advances in therapy · 2026Article
- Review no. 1: designing clinical kidney research using real-world data: research questions, data sources, and analytical skills.Clinical and experimental nephrology · 2026Review
- Circulating Humanin Improves the Prognostic Accuracy of Cardiovascular Risk Models in Chronic Hemodialysis Patients.Cardiorenal medicine · 2026Article
- Predicting cardiovascular events in hemodialysis patients based on the fusion of physicochemical indicators and tongue images: a prospective and multicenter study.Frontiers in physiology · 2026Article
- A time-dependent predictive model for cardiocerebral vascular events in chronic hemodialysis patients: insights from a prospective study.Frontiers in medicine · 2025Article
- Phase angle is a useful predicting indicator for protein-energy wasting and cardiovascular risk among maintenance hemodialysis patients.Scientific reports · 2024Observational
- External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network.Clinical kidney journal · 2024Article
- Development and validation of a model for predicting the risk of cardiovascular events in maintenance hemodialysis patients.Scientific reports · 2024Article
- Elevated HIF-1α levels in maintenance hemodialysis patients: a potential link to increased cognitive impairment risk.Frontiers in aging neuroscience · 2024Article
- The prevalence of dyslipidemia in patients on hemodialysis: a cross-sectional study from Syria.Annals of medicine and surgery (2012) · 2023Article
- Article
- Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis-Treated Patients Using Stacked Generalization: Model Development and Validation Study.JMIR medical informatics · 2021Article
- Bioelectrical impedance analysis-derived phase angle as a determinant of protein-energy wasting and frailty in maintenance hemodialysis patients: retrospective cohort study.BMC nephrology · 2020Observational
- Intra-Dialytic Hemoglobin Changes and Cardiovascular Events: A Cohort Study on Dialysis Outcomes and Practice Patterns in Japan.American journal of nephrology · 2019Article
- Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients.PloS one · 2019Article
- Association between circulating fibroblast growth factor 21 and mortality in end-stage renal disease.PloS one · 2017Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular (CV) events are the primary cause of death and becoming bedridden among hemodialysis (HD) patients. The Framingham risk score (FRS) is useful for predicting incidence of CV events in the general population, but is considerd to be unsuitable for the prediction of the incidence of CV events in HD patients, given their characteristics due to atypical relationships between conventional risk factors and outcomes. We therefore aimed to develop a new prognostic prediction model for prevention and early detection of CV events among hemodialysis patients.
methodsWe enrolled 3,601 maintenance HD patients based on their data from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS), phases 3 and 4. We longitudinaly assessed the association between several potential candidate predictors and composite CV events in the year after study initiation. Potential candidate predictors included the component factors of FRS and other HD-specific risk factors. We used multivariable logistic regression with backward stepwise selection to develop our new prediction model and generated a calibration plot. Additinially, we performed bootstrapping to assess the internal validity.
resultsWe observed 328 composite CV events during 1-year follow-up. The final prediction model contained six variables: age, diabetes status, history of CV events, dialysis time per session, and serum phosphorus and albumin levels. The new model showed significantly better discrimination than the FRS, in both men (c-statistics: 0.76 for new model, 0.64 for FRS) and women (c-statistics: 0.77 for new model, 0.60 for FRS). Additionally, we confirmed the consistency between the observed results and predicted results using the calibration plot. Further, we found similar discrimination and calibration to the derivation model in the bootstrapping cohort.
conclusionsWe developed a new risk model consisting of only six predictors. Our new model predicted CV events more accurately than the FRS.
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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 OpenQuestion graph.