TY - JOUR
T1 - Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease
T2 - The CRIC Study
AU - Bundy, Joshua D.
AU - Rahman, Mahboob
AU - Matsushita, Kunihiro
AU - Jaeger, Byron C.
AU - Cohen, Jordana B.
AU - Chen, Jing
AU - Deo, Rajat
AU - Dobre, Mirela A.
AU - Feldman, Harold I.
AU - Flack, John
AU - Kallem, Radhakrishna R.
AU - Lash, James P.
AU - Seliger, Stephen
AU - Shafi, Tariq
AU - Weiner, Shoshana J.
AU - Wolf, Myles
AU - Yang, Wei
AU - Allen, Norrina B.
AU - Bansal, Nisha
AU - He, Jiang
N1 - Publisher Copyright:
© 2022 by the American Society of Nephrology.
PY - 2022/3
Y1 - 2022/3
N2 - 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]50.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P50.03), 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 (P50.001). 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.
AB - 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]50.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P50.03), 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 (P50.001). 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.
UR - http://www.scopus.com/inward/record.url?scp=85125254341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125254341&partnerID=8YFLogxK
U2 - 10.1681/ASN.2021060747
DO - 10.1681/ASN.2021060747
M3 - Article
C2 - 35145041
AN - SCOPUS:85125254341
SN - 1046-6673
VL - 33
SP - 601
EP - 611
JO - Journal of the American Society of Nephrology
JF - Journal of the American Society of Nephrology
IS - 3
ER -