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Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and
Yong-Soo Baek1,2,3, Dong-Ho Lee2, Yoonsu Jo2
1Division of Cardiology, Department of Internal Medicine, Inha University College of Medicine and Inha University Hospital, Incheon, South Korea.
Insights
Artificial intelligence-estimated heart age from ECGs predicts mortality and cardiovascular events. An AI ECG-heart age significantly older than chronological age increases risks, while a younger age shows inverse effects.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Limited data exists on artificial intelligence-estimated biological heart age (AI ECG-heart age) for predicting cardiovascular outcomes, differentiating it from chronological age (CA).
- A deep learning algorithm was developed to estimate AI ECG-heart age using standard 12-lead ECGs.
- The study aimed to evaluate the predictive capability of AI ECG-heart age for mortality and cardiovascular outcomes.
Purpose of the Study:
- To develop and validate a deep learning model for estimating biological heart age from ECGs.
- To assess the association between AI ECG-heart age and all-cause mortality.
- To investigate the relationship between AI ECG-heart age and major adverse cardiovascular events (MACEs).
Main Methods:
- A deep neural network was trained and validated on 425,051 12-lead ECGs (2006-2021).
- A holdout test set of 97,058 ECGs was used for validation.
- Cox proportional hazards models were employed to analyze outcomes after adjusting for comorbidities.
Main Results:
- The AI ECG-heart age algorithm demonstrated a mean absolute error of 5.8 ± 3.9 years.
- Patients with AI ECG-heart age 6 years older than CA had significantly higher all-cause mortality (HR 1.60) and MACEs (HR 1.91).
- Conversely, patients with AI ECG-heart age younger than CA showed reduced mortality (HR 0.82) and MACEs (HR 0.78).
- ECG features like PR interval, QRS duration, and QT/QTc intervals showed alterations with increasing AI ECG-heart age.
Conclusions:
- AI-estimated biological heart age significantly impacts mortality and MACEs.
- AI ECG-heart age serves as a valuable tool for primary prevention and cardiovascular healthcare.
- The findings highlight the potential of AI in assessing cardiovascular risk beyond chronological age.
Background:
There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.
Methods:
We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].
Findings:
In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.
Conclusion:
Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.
