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. 2024 Sep 5;7(1):234.
doi: 10.1038/s41746-024-01234-1.

Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation

Hanjin Park #  1 , Oh-Seok Kwon #  1 , Jaemin Shim  2 , Daehoon Kim  1 , Je-Wook Park  1 , Yun-Gi Kim  3 , Hee Tae Yu  1 , Tae-Hoon Kim  1 , Jae-Sun Uhm  1 , Jong-Il Choi  3 , Boyoung Joung  1 , Moon-Hyoung Lee  1 , Hui-Nam Pak  4
Affiliations

Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation

Hanjin Park et al. NPJ Digit Med. .

Abstract

The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Study flow chart.
AI-ECG prediction model was validated in four independent multinational datasets and was then tested on two independent AF ablation cohorts. AI artificial intelligence, AF atrial fibrillation, UKB UK Biobank.
Fig. 2
Fig. 2. 5-year cumulative event rate of clinical recurrence in the Aged ECG and normal ECG age group in the YUHS discovery and KUAH evaluation cohort is presented.
Aged ECG was defined as AI-ECG age gap of ≥10 years and normal ECG age was defined as AI-ECG age gap of < 10 years. 5-year cumulative event rate of clinical recurrence stratified by AI-ECG age gap in a YUHS discovery and b KUAH evaluation cohorts. Abbreviations are the same as in Table 2.
Fig. 3
Fig. 3. 5-year cumulative event rate of clinical recurrence in the Aged ECG and normal ECG age group according to chronological age and LA diameter in the YUHS discovery and KUAH evaluation cohort is presented.
Aged ECG was defined as AI-ECG age gap of ≥10 years and normal ECG age was defined as AI-ECG age gap of <10 years. 5-year cumulative event rate of clinical recurrence stratified by AI-ECG age gap and a median chronological age or b median LA diameter in the YUHS discovery and KUAH evaluation cohorts Abbreviations are the same as in Table 2.
Fig. 4
Fig. 4. Subgroups analysis of the 5-year risk of clinical recurrence stratified by AI-ECG age gap in the YUHS discovery and KUAH evaluation cohorts.
Hazard ratios for 5-year risk of clinical recurrence in the Aged ECG compared with the normal ECG age group in the YUHS discovery and KUAH evaluation cohort is presented. Hazard ratios were adjusted for AF type, age, sex, body mass index, hypertension, diabetes, vascular disease, heart failure, LA diameter, E/e’, and LV ejection fraction. CI confidence interval. Other abbreviations are the same as in Table 2.
Fig. 5
Fig. 5. Correlation between conventional ECG parameters and (a) AI-ECG age gap, (b) AI-ECG age, (c) chronological age, and (d) AI-ECG age gap group.
bpm beats per minute, ms milliseconds. Other abbreviations are the same as in Table 2.
See this image and copyright information in PMC

References

    1. Marrouche, N. F. et al. Catheter ablation for atrial fibrillation with heart failure. N. Engl. J. Med.378, 417–427 (2018). 10.1056/NEJMoa1707855 - DOI - PubMed
    1. Kirchhof, P. et al. Early rhythm-control therapy in patients with atrial fibrillation. N. Engl. J. Med.383, 1305–1316 (2020). 10.1056/NEJMoa2019422 - DOI - PubMed
    1. Kim, D. et al. Treatment timing and the effects of rhythm control strategy in patients with atrial fibrillation: nationwide cohort study. BMJ373, n991 (2021). 10.1136/bmj.n991 - DOI - PMC - PubMed
    1. Hindricks, G. et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur. Heart J.42, 373–498 (2021). 10.1093/eurheartj/ehaa612 - DOI - PubMed
    1. Joglar, J. A. et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation149, e1–e156 (2024). 10.1161/CIR.0000000000001193 - DOI - PMC - PubMed

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