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Published on: September 26, 2018
Universal Risk Prediction for Individuals With and Without Atherosclerotic Cardiovascular Disease
Yejin Mok1, Zeina Dardari2, Yingying Sang1
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Insights
A new universal risk prediction model effectively assesses major adverse cardiovascular events (MACEs) in individuals with and without atherosclerotic cardiovascular disease (ASCVD). This approach simplifies risk stratification for both primary and secondary prevention strategies.
Area of Science:
- Cardiology
- Preventive Medicine
- Biostatistics
Background:
- Current guidelines recommend separate risk classification systems for primary and secondary cardiovascular disease prevention.
- These systems often utilize similar predictors, suggesting potential for a unified approach to predicting major adverse cardiovascular events (MACEs).
Purpose of the Study:
- To evaluate predictor performance in individuals with and without existing atherosclerotic cardiovascular disease (ASCVD).
- To develop and validate a universal risk prediction model for MACEs applicable across different ASCVD statuses.
Main Methods:
- Analysis of 9,138 participants from the ARIC study (with and without ASCVD) using Cox models.
- Inclusion of established predictors, body mass index, and cardiac biomarkers (troponin, natriuretic peptide).
- Validation of the universal model in the MESA study.
Main Results:
- Most predictors demonstrated similar associations with MACEs regardless of baseline ASCVD status.
- The universal risk prediction model exhibited good discrimination (c-statistics 0.747 and 0.691) and excellent calibration for both groups.
- The model identified individuals without ASCVD at higher risk than some with ASCVD and was externally validated.
Conclusions:
- A universal risk prediction approach is effective for individuals with and without ASCVD.
- This unified model can streamline risk classification, aiding the transition between primary and secondary cardiovascular prevention.
Background:
American College of Cardiology/American Heart Association guidelines recommend distinct risk classification systems for primary and secondary cardiovascular disease prevention. However, both systems rely on similar predictors (eg, age and diabetes), indicating the possibility of a universal risk prediction approach for major adverse cardiovascular events (MACEs).
Objectives:
The authors examined the performance of predictors in persons with and without atherosclerotic cardiovascular disease (ASCVD) and developed and validated a universal risk prediction model.
Methods:
Among 9,138 ARIC (Atherosclerosis Risk In Communities) participants with (n = 609) and without (n = 8,529) ASCVD at baseline (1996-1998), we examined established predictors in the risk classification systems and other predictors, such as body mass index and cardiac biomarkers (troponin and natriuretic peptide), using Cox models with MACEs (myocardial infarction, stroke, and heart failure). We also evaluated model performance.
Results:
Over a follow-up of approximately 20 years, there were 3,209 MACEs (2,797 for no prior ASCVD). Most predictors showed similar associations with MACE regardless of baseline ASCVD status. A universal risk prediction model with the predictors (eg, established predictors, cardiac biomarkers) identified by least absolute shrinkage and selection operator regression and bootstrapping showed good discrimination for both groups (c-statistics of 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 validated externally in 5,322 participants in the MESA (Multi-Ethnic Study of Atherosclerosis).
Conclusions:
A universal risk prediction approach performed well in persons with and without ASCVD. This approach could facilitate the transition from primary to secondary prevention by streamlining risk classification and discussion between clinicians and patients.
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