aceModerate evidence
Published recordHealthGood Space (a documented gain)
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Leveraging AI-ECG Technology for Early Notification and Tracking of AF Development (LATENT): A Randomized Controlled Trial
- Truvace record
- TRV-2026-2462raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Randomized controlled trial
- Sources
- 1 sourcePeer-reviewed
- sha256
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Background Electrocardiographic analysis aided by artificial intelligence (AI) may identify atrial electrical vulnerability during sinus rhythm. The objective of this study was to determine whether AI-enabled ECG (AI-ECG) is effective for detecting atrial fibrillation (AF), atrial flutter (AFl), or prespecified pre-AF atrial findings in a randomized trial. Methods In this single-center, open-label trial, 14 665 adults 40 to 85 years of age without previous AF/AFl were randomized to an AI-ECG-guided strategy or u…
The gain
In a randomized trial of 14,665 adults, AI-ECG-guided monitoring detected new atrial fibrillation, flutter or pre-AF findings in 0.74% of intervention participants versus 0.48% of controls.
What this doesn’t fix
The authors say the findings were statistically fragile: reclassifying a single intervention event changed the P value from 0.046 to 0.057, and larger trials are needed to assess patient outcomes.
Sources
1 on file- Circulationdoi:10.1161/circulationaha.126.083540
Cite this record
v1Truvace Impact Record TRV-2026-2462, v1: “Leveraging AI-ECG Technology for Early Notification and Tracking of AF Development (LATENT): A Randomized Controlled Trial.” Truvace, 2026-11-11. /record/TRV-2026-2462 (accessed on the day you copy it). sha256 3be64760949f1d2c…
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