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A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms

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Abstract

Background

The wearable cardioverter defibrillator (WCD) is an important tool in mitigating sudden cardiac death (SCD). The WCD provides patient alarms for detected arrhythmias or electrical noise/artifact. Some patients experience frequent alarms for artifact. We sought to evaluate the effects of a novel artificial intelligence algorithm to reduce alarms related to electrical noise or artifact (advanced arrhythmia discrimination algorithm, AArD).

Methods

A retrospective review of a large commercial database of prescribed WCD. Patients prescribed the WCD during the years 2017 (discrimination algorithm, DA group) or during 2019 (advanced arrhythmia discrimination algorithm, AArD) were analyzed. A total of 96,000 patients were sampled, 48,000 in the control group (using standard direct algorithm, DA) and compared to 48,000 in the intervention AArD (4000 per group per month) for 12 months. The AArD further discriminates ECG signals based on a machine-learning algorithm utilizing intensity and frequency beyond the standard DA. Outcomes regarding alarms, arrhythmias, and safety were analyzed.

Results

The AArD algorithm was associated with a significant decrease in frequency of alarms over the course of WCD use; 54% of patients in the AARD versus 27% of DA had 0 alarms (P < .001). In the entire cohort, there was a 56% relative reduction in alarms with the use of AArD. Appropriate arrhythmia treatment time was not significantly different between the groups (44 s DA vs 45 s AArD [p = ns]).

Conclusions

A novel artificial intelligence algorithm reduces alarms without delaying appropriate therapy for VT/VF. These changes may improve compliance and quality of life in patients with a WCD.

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Authors and Affiliations

  1. Electrophysiology Section, Cardiovascular Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Jeffrey Arkles & Benjamin D’Souza

  2. Baylor Heart and Vascular Hospital, Fort Worth, TX, USA

    Craig Delaughter

  3. Penn Presbyterian Medical Center, Heart and Vascular Pavilion, 51 N. 39th Street, 4th floor, Philadelphia, PA, 19104, USA

    Benjamin D’Souza

Authors
  1. Jeffrey Arkles
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  2. Craig Delaughter
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Corresponding author

Correspondence to Benjamin D’Souza.

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Ethical approval

The research reported in this paper adhered to PRISMA guidelines. This study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Institutional Review Board for each of the authors listed was not required given that the data is not associated with any of the listed institutions.

Informed consent

All patients provided informed consent at the time they received the WCD for quality monitoring, healthcare operations, and research purposes.

Conflict of interest

The authors declare no competing interests.

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Arkles, J., Delaughter, C. & D’Souza, B. A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms. J Interv Card Electrophysiol 66, 1723–1728 (2023). https://doi.org/10.1007/s10840-023-01497-w

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  • DOI: https://doi.org/10.1007/s10840-023-01497-w

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