Key result
A deep-learning model accurately predicted age and sex from reduced-lead ECGs, and a predicted age higher than actual age was associated with increased mortality risk (HR 2.49 for 12-lead).
Why the study?
Applying artificial intelligence to infer health-related information from 12-lead ECGs has gained interest, motivating investigation into whether deep learning can predict sex, age, and mortality from reduced-lead ECG configurations.
Can a deep-learning model accurately predict age and sex from reduced-lead ECGs and provide insights into patient mortality?
Observational
Can a deep-learning model accurately predict age and sex from reduced-lead ECGs and provide insights into patient mortality?
Hazard Ratio: 2.49
Deep learning models can accurately predict age and sex from reduced-lead ECGs, and discrepancies between predicted and actual age/sex are associated with increased mortality risk.
AI sex/age prediction from reduced-lead ECGs is feasible; leaves open clinical utility and need for prospective validation.
The resting 12-lead electrocardiogram (ECG) is a widely used diagnostic tool in modern medicine, providing crucial insights into various heart conditions. Recently, the application of Artificial Intelligence (AI) to infer health-related information from the 12-lead ECG has gained significant interest. In this study, we propose a deep-learning approach to predict sex and age from both 12-lead and reduced-lead ECGs (12L, 6L, 4L, 3L, 2L, and 1L), and analyze their implications for predicting patient mortality. We employed a ResNeXt-based architecture and trained our model using the CODE15 dataset. Our best sex prediction model achieved an F1-score of 0.800 ± 0.007, Sensitivity (Se) of 0.807 ± 0.016, Positive Predictive Value (PPV) of 0.793 ± 0.022, and an Area Under the Curve (AUC) of 0.910 ± 0.007 when using the 12-lead ECG configuration. Similarly, our best age estimation model achieved a mean absolute error (MAE) of 8.961 ± 0.180, a Pearson Correlation (ρ) of 0.810 ± 0.004, and a coefficient of determination (R2) of 0.637 ± 0.014 when using the 4-lead ECG configuration. Through the evaluation of different lead-set configurations, we demonstrated that even with a reduced number of leads, our models achieved comparable performance to those ob- tained using the conventional 12-lead ECG setup. Moreover, we found that the mortality risk, assessed by the hazard ratio (HR), increased when our age model predicted an age higher than the actual age by a certain threshold for all lead sets (12L: 2.49, 6L: 2.17, 4L: 2.53, 3L: 2.54, 2L: 2.76, 1L: 2.65). Likewise, when our model misclassified the patient’s actual sex, the mortality risk also increased (12L: 1.36, 6L: 1.26, 4L: 1.38, 3L: 1.33, 2L: 1.20, 1L: 1.12). Additionally, we observed a decrease in mortality risk when our method predicted an age lower than the actual age by a certain threshold (12L: 0.71, 6L: 0.71, 4L: 0.74, 3L: 0.71, 2L: 0.75, 1L: 0.64). Overall, our research shows the efficacy of reduced lead ECGs in predicting age, determining sex, and providing valuable insights into patient mortality.
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Dias et al. (2023) reported an observational. Deep-learning model predicting age higher than actual age from ECG vs. Actual age or lower predicted age was evaluated on Mortality risk when predicted age is higher than actual age (12-lead) (HR 2.49). A deep-learning model accurately predicted age and sex from reduced-lead ECGs, and a predicted age higher than actual age was associated with increased mortality risk (HR 2.49 for 12-lead).
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