Universal Atherosclerotic CVD Risk Prediction

Quick Takes

  • A universal risk prediction model with identified predictors (e.g., established predictors, cardiac biomarkers) showed good discrimination for both groups (c-statistic 0.747 and 0.691, respectively) in the prediction of MACE (CHD, stroke, and HF).
  • Most ASCVD predictors were similarly associated with MACE regardless of baseline ASCVD status.
  • This prediction tool may allow for the identification of adults with and without a history of ASCVD who are at higher risk for future events.

Study Questions:

Can a universal risk prediction model be developed for persons with and without atherosclerotic cardiovascular disease (ASCVD)?


Data from the observational cohort of the ARIC (Atherosclerosis Risk in Communities) study were used for the present analysis. ARIC participants aged 45-64 years at visit 1 (1987-89) were enrolled from four US communities. Follow-up exams were completed in 1990-92 (visit 2), 1993-95 (visit 3), and 1996-98 (visit 4). Annual telephone calls were conducted between visits. For the present analysis, visit 4 was used as the baseline. Of 11,656 participants who attended visit 4, non-White/non-Black participants (n = 31) and those with data for CVD risk factors (n = 2,285) were excluded, leaving a final study cohort of 9,138.

Predictors of ASCVD risk examined included body mass index, cardiac biomarkers (i.e., troponin and N-terminal pro–B-type natriuretic peptide [NT-proBNP]), family history of premature ASCVD, high-sensitivity C-reactive protein (hsCRP), lipoprotein(a), in addition to traditional risk factors such as age, sex, race, diabetes, systolic blood pressure (or use of antihypertensive medication), lipids (and statin use), estimated glomerular filtration rate, smoking status, and history of heart failure (HF). The outcome of interest was major adverse cardiovascular events (MACE), defined as myocardial infarction, ischemic stroke, systematic peripheral artery disease, and heart failure (identified through hospital discharge codes).


A total of 9,138 participants were included in the present analysis (mean age 62.9 [standard deviation 5.7 years), 57% were women, and 20% were Black. A history of ASCVD was present in 609 (6.7%) of the analytic cohort. Participants with ASCVD were more likely to be older adults, male, and have a higher prevalence of antihypertensive medication use, diabetes, statin use, a family history of premature ASCVD, and a prior history of HF. Participants with ASCVD were also more likely to have higher hsCRP, lipoprotein(a), triglycerides, cardiac biomarkers (i.e., NT-proBNP and high-sensitivity cardiac troponin T), and a lower level of total cholesterol and high-density lipoprotein cholesterol.

Over a median follow-up of 18.9 years, 3,209 MACE events (2,797 for no prior ASCVD) occurred. Most predictors showed similar associations with MACE regardless of baseline ASCVD status. A universal risk prediction model with identified predictors (e.g., established predictors, cardiac biomarkers) showed good discrimination for both groups (c-statistic 0.747 and 0.691, respectively), and risk classification, and showed excellent calibration irrespective of ASCVD status. This universal prediction approach identified individuals without ASCVD who had a higher risk than some individuals with ASCVD and was externally validated in 5,322 MESA (Multi-Ethnic Study of Atherosclerosis) participants.


The authors conclude that a universal risk prediction approach exhibited good performance for predicting those at risk for future ASCVD events, regardless of a prior history of ASCVD. This approach could facilitate the transition from primary to secondary prevention by streamlining risk classification and discussion between clinicians and patients.


This analysis suggests that risk prediction can be performed in adults regardless of ASCVD status. Future analysis including cost-effectiveness would be warranted to understand the best methods for incorporation of a universal risk prediction approach in clinical care.

Clinical Topics: Prevention

Keywords: Atherosclerosis, Heart Disease Risk Factors, Secondary Prevention

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