Background Detecting significant coronary artery disease (CAD) in the general population is complex and relies on combined assessment of traditional CAD risk factors and noninvasive testing. We hypothesized that a CAD-specific heart rate variability (HRV) algorithm can be used to improve detection of subclinical or early ischemia in patients without known CAD. Methods and Results Between 2014 and 2018 we prospectively enrolled 1043 patients with low to intermediate pretest probability for CAD who were screened for myocardial ischemia in tertiary medical centers in the United States and Israel. Patients underwent 1-hour Holter testing, with immediate HRV analysis using the HeartTrends DyDx algorithm, followed by exercise stress echocardiography (n=612) or exercise myocardial perfusion imaging (n=431). The threshold for low HRV was identified using receiver operating characteristic analysis based on sensitivity and specificity. The primary end point was the presence of myocardial ischemia detected by exercise stress echocardiography or exercise myocardial perfusion imaging. The mean age of patients was 61 years and 38% were women. Myocardial ischemia was detected in 66 (6.3%) patients. After adjustment for CAD risk factors and exercise stress testing results, low HRV was independently associated with a significant 2-fold increased likelihood for myocardial ischemia (odds ratio, 2.00; 95% CI, 1.41-2.89 [P=0.01]). Adding HRV to traditional CAD risk factors significantly improved the pretest probability for myocardial ischemia. Conclusions Our data from a large prospective international clinical study show that short-term HRV testing can be used as a novel digital-health modality for enhanced risk assessment in low- to intermediate-risk individuals without known CAD. Clinical Trial Registration URL: http://www.ClinicalTrials.gov. Unique identifiers: NCT01657006, NCT02201017).
- coronary artery disease
- heart rate variability
- risk prediction
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine