CVD Risk Estimates for the US CKD Population with the American Heart Association PREVENT Equation

Background: Cardiovascular disease (CVD) risk equations can be useful for assessing and mitigating risk in people with CKD. The 2023 AHA PREVENT equations are the first major equations to incorporate the kidney metrics eGFR and UACR. We studied change in 10-year CVD risk prediction estimates in the US CKD population comparing the PREVENT equation to the previous major equation (ASCVD Risk Estim. Plus 2018). Methods: We calculated US CKD population estimates of 10-year CVD risk using the most recent data (2017-2020) from NHANES, a probability sampled study of the US community population. CVD risk was calculated using the PREVENT equation (with eGFR and UACR) and the prior major equation (ASCVD Risk Estim. Plus) for the relevant CKD population (age 30-79 years without clinical CVD). CKD was identified using eGFR-Cr and UACR and categorized by G stage. Appropriate survey methods were used to obtain cross-sectional population estimates. Results: We estimated 21.5 (95%CI 18.6-24.4) million individuals with CKD age 30-79 years without known CVD. Mean±SD age was 58±15 years and 58% were female. CKD stage distribution was: G1/2 A2/3 65%, G3a 26%, 5.8% G3b, 1.8% G4, and 1.2% G5. Median (IQR) 10-year risk in the CKD population using PREVENT is 13% (4.6-21%), substantially higher than the estimates using the prior equation: 9.2% (2.8-22%). More severe CKD stages had larger increase in risk with the new equation (Panel A). Using 10-year CVD risk thresholds (low<5%, borderline 5-7.4%, intermediate 7.5-19.9%, high ≥20%), 30% (6.5 million individuals) changed risk threshold group, with 25% moving to a higher risk category and 5.7% to a lower risk category (Panel B). Conclusion: Nearly 1 in 3 individuals with CKD in the US are estimated to have substantial change in predicted CVD risk with the PREVENT equation incorporating eGFR and UACR, with most moving to a higher risk category. This highlights the public health importance of kidney measurements for risk prognostication and mitigation. Funding: NIDDK Support