Why the study?
Atrial fibrillation detection is clinically challenging due to being frequently paroxysmal and asymptomatic, and many novel mHealth screening technologies have not been clinically validated.
Does a single-lead mHealth patch with AI analysis accurately detect atrial fibrillation compared to a 3-lead Holter monitor in emergency care patients?
Does a single-lead mHealth patch with AI analysis accurately detect atrial fibrillation compared to a 3-lead Holter monitor in emergency care patients?
A 24-hour mHealth patch with AI analysis demonstrated high sensitivity and specificity for detecting atrial fibrillation, suggesting it is a feasible tool for AF screening.
Answers are grounded in this paper and cite their sources.
Supports mHealth patch feasibility for emergency AF screening; leaves open prospective validation before practice change.
BACKGROUND The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. OBJECTIVE The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. METHODS Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. RESULTS Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. CONCLUSIONS The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. CLINICALTRIAL ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335
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Santala et al. (2021) studied this question.
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