Evaluation of Risk Prediction Models of Atrial Fibrillation (from the Multi-Ethnic Study of Atherosclerosis [MESA]).

ABSTRACT: 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 ho

SUBMITTER: Bundy JD 

PROVIDER: S-EPMC6911821 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature