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Accounting for inconclusive results and repeated testing: A framework for evaluating wearable electrocardiogram
Peter Doggart1, Caitlin Fisher2, Pardis Biglarbeigi3
1PulseAI Ltd, Belfast, United Kingdom; Ulster University, Belfast, United Kingdom.
Background:
Conventional wearable electrocardiogram (ECG) validation excludes inconclusive results and assumes single-attempt testing, which inflates reported diagnostic performance for atrial fibrillation (AF) detection. Current reporting frameworks do not reflect real-world clinical use.
Objective:
The study aimed to introduce and evaluate an intention-to-diagnose (ITD) framework incorporating inconclusive outputs and repeat testing for realistic assessment of wearable ECG diagnostic performance in AF detection.
Methods:
Prospective observational study comparing 3 reporting frameworks-naive (exclude inconclusive), pragmatic (count as incorrect), and ITD (permit 3 attempts), applied to identical Apple Watch ECG recordings using native algorithm and artificial intelligence (AI)-enabled neural network. Study conducted at a teaching hospital in Ireland with 296 participants after exclusions.
Results:
Apple Watch naive sensitivity/specificity were 96.1%/97.9%; pragmatic 78.1%/81.0%; ITD 92.2%/91.0%. The AI algorithm achieved pragmatic 98.4%/96.1% and ITD 98.4%/96.6%, with 92% reduction in inconclusive outputs vs Apple Watch. Repeatability was substantial for Apple Watch (kappa 0.77) and near-perfect for AI (0.96).
Conclusion:
Wearable ECG reporting should adopt ITD frameworks, explicit handling of inconclusive outputs and repeat testing. Naive reporting inflates performance, whiereas pragmatic reporting deflates it. AI-enhanced interpretation materially reduces inconclusive results and improves ITD accuracy, providing a more usable and reliable pathway for real-world AF detection.
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