Atrial fibrillation (AF) is prevalent and strongly associated with higher cardiovascular disease (CVD) risk. Machine learning is increasingly used to identify novel predictors of CVD risk, but prediction improvements beyond established risk scores are uncertain. We evaluated improvements in predicting 5-year AF risk when adding novel candidate variables identified by machine learning to the CHARGE-AF Enriched score, which includes age, race/ethnicity, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and NT-proBNP. We included 3,534 participants (mean age, 61.3 years; 52.0% female) with complete data from the prospective Multi-Ethnic Study of Atherosclerosis. Incident AF was defined based on study electrocardiograms and hospital discharge diagnosis ICD-9 codes, supplemented by Medicare claims. Prediction performance was evaluated using Cox regression and a parsimonious model was selected using LASSO. Within 5 years of baseline, 124 participants had incident AF. Compared with the CHARGE-AF Enriched model (c-statistic, 0.804), variables identified by machine learning, including biomarkers, cardiac magnetic resonance imaging variables, electrocardiogram variables, and subclinical CVD variables, did not significantly improve prediction. A 23-item score derived by machine learning achieved a c-statistic of 0.806, whereas a parsimonious model including the clinical risk factors age, weight, current smoking, NT-proBNP, coronary artery calcium score, and cardiac troponin-T achieved a c-statistic of 0.802. This analysis confirms that the CHARGE-AF Enriched model and a parsimonious 6-item model performed similarly to a more extensive model derived by machine learning. In conclusion, these simple models remain the gold standard for risk prediction of AF, although addition of the coronary artery calcium score should be considered.
Bibliographical noteFunding Information:
Sources of Funding: This research was supported by grant R01HL127659 from the National Heart, Lung, and Blood Institute. The Multi-Ethnic Study of Atherosclerosis (MESA) was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from NCATS. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. Dr. Bundy is supported by the National Heart, Lung, and Blood Institute Cardiovascular Epidemiology training grant T32HL069771. Dr. Chen is supported by R01HL126637 and R01HL141288 from the National Heart, Lung, and Blood Institute.
PubMed: MeSH publication types
- Journal Article
- Multicenter Study
- Observational Study
- Research Support, N.I.H., Extramural