Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias.

Autor: Kolk MZH; Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands., Frodi DM; Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Inge Lehmanns Vej 7, 2100, Copenhagen, Denmark., Langford J; Activinsights Ltd., Unit 11, Harvard Industrial Estate, Kimbolton, Huntingdon, PE28 0NJ, United Kingdom.; College of Life and Environmental Sciences, University of Exeter, Stocker Rd, Exeter, EX4 4PY, United Kingdom., Andersen TO; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark., Jacobsen PK; Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Inge Lehmanns Vej 7, 2100, Copenhagen, Denmark., Risum N; Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Inge Lehmanns Vej 7, 2100, Copenhagen, Denmark., Tan HL; Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands.; Netherlands Heart Institute, Moreelsepark 1, 3511 EP, Utrecht, The Netherlands., Svendsen JH; Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Inge Lehmanns Vej 7, 2100, Copenhagen, Denmark.; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark., Knops RE; Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands., Diederichsen SZ; Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Inge Lehmanns Vej 7, 2100, Copenhagen, Denmark., Tjong FVY; Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands. f.v.tjong@amsterdamumc.nl.; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands. f.v.tjong@amsterdamumc.nl.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2024 Sep 16; Vol. 7 (1), pp. 250. Date of Electronic Publication: 2024 Sep 16.
DOI: 10.1038/s41746-024-01247-w
Abstrakt: We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54-8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.
(© 2024. The Author(s).)
Databáze: MEDLINE
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