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Wearable-Derived Long-Term Heart Rate Variability Predicts Major Adverse Cardiovascular Events in Middle Aged Individuals Without Previous Cardiovascular Disease

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 Green open access

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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.

Type: Proceedings paper
Title: Wearable-Derived Long-Term Heart Rate Variability Predicts Major Adverse Cardiovascular Events in Middle Aged Individuals Without Previous Cardiovascular Disease
Event: 50th Computing in Cardiology Conference 2023
ISBN-13: 9798350382525
Open access status: An open access version is available from UCL Discovery
DOI: 10.22489/CinC.2023.177
Publisher version: http://dx.doi.org/10.22489/cinc.2023.177
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing
URI: https://discovery.ucl.ac.uk/id/eprint/10186375
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