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Kidney Week

Abstract: SA-PO0394

AHA PREVENT Equations and Coronary Artery Calcium for Atherosclerotic Cardiovascular Disease Risk Prediction in CKD

Session Information

Category: Cardiovascular-Kidney-Metabolic Health

  • 602 Cardiovascular-Kidney-Metabolic Health: Clinical

Authors

  • Krishnan, Vaishnavi, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, United States
  • Cai, Xuan, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
  • Shah, Nilay, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
  • Huang, Xiaoning, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
  • Cohen, Debbie L., University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
  • Rosas, Sylvia E., Harvard Medical School, Boston, Massachusetts, United States
  • Navaneethan, Sankar D., Baylor College of Medicine, Houston, Texas, United States
  • He, Jiang, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
  • Chen, Jing, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
  • Rincon-Choles, Hernan, Cleveland Clinic, Cleveland, Ohio, United States
  • Rao, Panduranga S., University of Michigan Medical School, Ann Arbor, Michigan, United States
  • Lash, James P., University of Illinois Chicago College of Medicine, Chicago, Illinois, United States
  • Khan, Sadiya, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
  • Mehta, Rupal, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
Background

Strategies are needed to identify the high risk of atherosclerotic cardiovascular disease (ASCVD) in patients with chronic kidney disease (CKD). Vascular calcification is accelerated in CKD, but how adding coronary artery calcium (CAC) scores to the 10-year PREVENT-ASCVD risk equations changes its predictive utility among patients with CKD is unknown.

Methods

In Chronic Renal Insufficiency Cohort (CRIC) participants without prior ASCVD, we calculated 10-year ASCVD risk using the American Heart Association (AHA) Predicting Risk of CVD EVENTs (PREVENT) urine-albumin creatinine ratio (UACR) add-on equation. We assessed model discrimination using Harrell’s C statistic and calculated net reclassification improvement (NRI) when CAC scores were added to 10-year PREVENT-ASCVD risk calculations.

Results

Among 1150 CRIC participants (mean [standard deviation] age 57.6 years [11.2], eGFR: 42.6 ml/min/1.73m2 [16.6], 10-year PREVENT-ASCVD risk: 12.9% [10.7]) over a median follow-up of 10 years, 139 ASCVD events occurred. The addition of CAC to PREVENT-ASCVD improved discrimination (change in C statistic +0.042), with a positive NRI of 13.6% (Figure).

Conclusion

Inclusion of CAC in PREVENT-ASCVD resulted in clinically-meaningful improvement and reclassification in 10-year ASCVD risk prediction among adults with CKD. These findings demonstrate the value of incorporating CAC measurement among patients with CKD, and contributes to risk stratification and preventive efforts for ASCVD in CKD.

Funding

  • NIDDK Support