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Title: 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).
Authors: Matsubara, Yukiko
Kimachi, Miho
Fukuma, Shingo  kyouindb  KAKEN_id
Onishi, Yoshihiro
Fukuhara, Shunichi  kyouindb  KAKEN_id
Author's alias: 福間, 真悟
Issue Date: 8-Mar-2017
Publisher: Public Library of Science
Journal title: PLOS ONE
Volume: 12
Issue: 3
Thesis number: e0173468
Abstract: Background: Cardiovascular (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. Methods: We 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. Results: We 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. Conclusions: We developed a new risk model consisting of only six predictors. Our new model predicted CV events more accurately than the FRS.
Rights: © 2017 Matsubara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI: http://hdl.handle.net/2433/219022
DOI(Published Version): 10.1371/journal.pone.0173468
PubMed ID: 28273175
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