Key result
Each 5-year increment in explainable A-ECG Heart Age Gap was associated with a 23% increased risk of death or heart failure hospitalization (HR 1.23).
Why the study?
To evaluate the prognostic value of an explainable advanced ECG (A-ECG) Heart Age and compare its performance to a deep neural network artificial intelligence (DNN-AI) Heart Age.
Does an explainable A-ECG Heart Age Gap predict death or heart failure hospitalization and correlate with cardiovascular risk factors in patients undergoing clinical CMR imaging?
Cohort (n=731)
No
Does an explainable A-ECG Heart Age Gap predict death or heart failure hospitalization and correlate with cardiovascular risk factors in patients undergoing clinical CMR imaging?
An explainable A-ECG Heart Age Gap is an intuitive metric that independently predicts heart failure hospitalization or death and correlates with cardiovascular risk factors, demonstrating prognostic value without the need for age adjustment unlike black-box DNN-AI approaches.
Aims Deep neural network artificial intelligence (DNN-AI)–based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and results Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7–6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13–1.34) and adjusted HR 1.11 (1.01–1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01–1.21)], but not in unadjusted analyses [HR 1.00 (0.93–1.08)], making it less easily applicable in clinical practice. Conclusion A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.
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Lindow et al. (2023) conducted a cohort in Cardiovascular disease risk (n=731). A-ECG Heart Age Gap (5-year increments) vs. Lower Heart Age Gap was evaluated on Death or heart failure hospitalization (HR 1.23, 95% CI 1.13-1.34). Each 5-year increment in explainable A-ECG Heart Age Gap was associated with a 23% increased risk of death or heart failure hospitalization (HR 1.23).
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