Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study. Issue 3 (March 2022)

• Record Type: Journal Article
• Authors: Bundy, Joshua D.; Rahman, Mahboob; Matsushita, Kunihiro; Jaeger, Byron C.; Cohen, Jordana B.; Chen, Jing; Deo, Rajat; Dobre, Mirela A.; Feldman, Harold I.; Flack, John; Kallem, Radhakrishna R.; Lash, James P.; Seliger, Stephen; Shafi, Tariq; Weiner, Shoshana J.; Wolf, Myles; Yang, Wei; Allen, Norrina B.; Bansal, Nisha; He, Jiang
• Is Part Of: Journal of the American Society of Nephrology. Volume 33:Issue 3(2022)
• Page Start: 601
• Page End: 611
• Publication Date: 2022-03
• Subjects: chronic kidney disease -- cardiovascular disease -- clinical epidemiology -- risk factors -- atherosclerosis
• DOI: 10.1681/ASN.2021060747
• Languages: English
• ISSNs: 1046-6673

Abstract

Significance Statement: Patients with CKD are typically considered to be at high risk for atherosclerotic cardiovascular disease, but CKD is a heterogeneous condition and there are no validated atherosclerotic cardiovascular disease risk stratification tools for this population. Our analysis of 2604 participants in the Chronic Renal Insufficiency Cohort study found that newly developed risk prediction models, using clinically available variables and novel biomarkers, improved discrimination, calibration, and reclassification of nonevents compared with the traditional American College of Cardiology/American Heart Association pooled cohort equations developed for the general population. The new equations may improve risk stratification in patients with CKD and improve shared decision making for preventive therapy to reduce atherosclerotic cardiovascular disease incidence and mortality. Visual Abstract: 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.