Joshua D Bundy1,2, Mahboob Rahman3, Kunihiro Matsushita4, Byron C Jaeger5, Jordana B Cohen6, Jing Chen1,2,7, Rajat Deo8, Mirela A Dobre3, Harold I Feldman9, John Flack10, Radhakrishna R Kallem6, James P Lash11, Stephen Seliger12, Tariq Shafi13, Shoshana J Weiner12, Myles Wolf14, Wei Yang9, Norrina B Allen15, Nisha Bansal16, Jiang He17,2,7. 1. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana. 2. Tulane University Translational Science Institute, New Orleans, Louisiana. 3. Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio. 4. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Division of Cardiology, Johns Hopkins School of Medicine, Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland. 5. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama. 6. Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. 7. Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana. 8. Cardiovascular Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. 9. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. 10. Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, Illinois. 11. Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois. 12. Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland. 13. Nephrology Division, The University of Mississippi Medical Center, Jackson, Mississippi. 14. Department of Medicine, Duke Clinical Research Institute, Duke University, Durham, North Carolina. 15. Department of Preventive Medicine, Northwestern Feinberg School of Medicine, Chicago, Illinois. 16. Division of Nephrology, University of Washington, Seattle, Washington. 17. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana jhe@tulane.edu.
Abstract
BACKGROUND: Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS: We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS: Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS: The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.
BACKGROUND: Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS: We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS: Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS: The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.
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