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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)
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)
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 3, p e0173468 (2017)
- Publication Year :
- 2016
-
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.
- Subjects :
- Male
medicine.medical_treatment
030232 urology & nephrology
lcsh:Medicine
Blood Pressure
030204 cardiovascular system & hematology
Logistic regression
Biochemistry
Vascular Medicine
0302 clinical medicine
Mathematical and Statistical Techniques
Endocrinology
Japan
Risk Factors
Outcome Assessment, Health Care
Odds Ratio
Medicine and Health Sciences
lcsh:Science
Statistical Data
education.field_of_study
Multidisciplinary
Framingham Risk Score
Incidence
Middle Aged
Prognosis
Lipids
Cholesterol
Cardiovascular Diseases
Nephrology
Population Surveillance
Cohort
Physical Sciences
Female
Hemodialysis
Risk assessment
Statistics (Mathematics)
Research Article
Adult
medicine.medical_specialty
Endocrine Disorders
Population
Research and Analysis Methods
Risk Assessment
03 medical and health sciences
Renal Dialysis
Internal medicine
Albumins
Medical Dialysis
medicine
Diabetes Mellitus
Humans
Statistical Methods
Intensive care medicine
education
Aged
business.industry
lcsh:R
Biology and Life Sciences
Proteins
Odds ratio
Stepwise regression
Metabolic Disorders
lcsh:Q
business
Mathematics
Forecasting
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....8acde0ac979baf93e217d182d61fdfe8