TY - JOUR
T1 - Artificial intelligence-electrocardiography to detect atrial fibrillation
T2 - Trend of probability before and after the first episode
AU - Christopoulos, Georgios
AU - Attia, Zachi I.
AU - Van Houten, Holly K.
AU - Yao, Xiaoxi
AU - Carter, Rickey E.
AU - Lopez-Jimenez, Francisco
AU - Kapa, Suraj
AU - Noseworthy, Peter A.
AU - Friedman, Paul A.
N1 - Publisher Copyright:
© 2022 The Author(s).
PY - 2022/6/1
Y1 - 2022/6/1
N2 - Aims: Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results: We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion: The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.
AB - Aims: Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results: We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion: The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.
KW - Artificial intelligence
KW - Atrial fibrillation
KW - Electrocardiography
KW - Machine learning
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U2 - 10.1093/ehjdh/ztac023
DO - 10.1093/ehjdh/ztac023
M3 - Article
AN - SCOPUS:85153738622
SN - 2634-3916
VL - 3
SP - 228
EP - 235
JO - European Heart Journal - Digital Health
JF - European Heart Journal - Digital Health
IS - 2
ER -