Orini, M;
Flores, JL;
Chaturvedi, N;
Hughes, A;
(2023)
Wearable-Derived Long-Term Heart Rate Variability Predicts Major Adverse Cardiovascular Events in Middle Aged Individuals Without Previous Cardiovascular Disease.
In:
Computing in Cardiology 2023 (CinC 2023).
Computing in Cardiology
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CinC2023-177.pdf - Accepted Version Download (376kB) | Preview |
Abstract
Wearable devices enable continuous heart rate (HR) monitoring at scale. However, it is unclear how long-term HR recorded with wearable devices can be harnessed to predict cardiovascular (CV) disease, especially in view of a lower accuracy and temporal resolution compared to clinical ECGs. We hypothesized that robust HRV estimator can identify individuals at higher risk of major adverse CV events (MACE) in the general population. In the National Survey of Health and Development (NSHD), the Actiheart monitor was used to measure 30-second averaged HR in 1,462 participants aged 60-64 (53.2% female) without previous CV disease for up to 5 days. The median absolute deviation of 5-min averaged HR (MADAHR) and median absolute deviation of 30-sec averaged successive HR differences (MADSDHR) were used as robust estimates of the established metrics SDANN and SDSD, respectively. After a median follow-up of 11.3 years, n=136 (9.3%) MACE occurred. Reduced MADAHR and MADSDHR were associated with MACE with hazard ratio (95% confidence interval) equal to 1.33(1.10-1.62, p < 0.01), and 2.15(1.39-3.32, p < 0.01) after adjusting for average heart rate, sex, body-mass index, hypertension, diabetes, and beta-blockers. These data demonstrate for the first time that wearable derived long-term HRV can predict CV events in the general population.
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