Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival

Abstract

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.

Keywords: Cardiovascular disease; Heart age; Risk prediction; Vascular age.

Graphical Abstract

Figure 1

Flowchart of patient inclusion and exclusion. DNN-AI ECG, deep neural network artificial intelligence electrocardiogram.

Figure 2

Time-to-event analysis for A-ECG Heart Age Gap (left panel) and DNN-AI ECG Heart Age Gap (right panel) regarding death or heart failure hospitalization among 731 patients who were referred for a clinical CMR imaging study. For both the A-ECG Heart Age and for the DNN-AI ECG Heart Age, patients are divided into three groups based on the difference between the Heart Age and their chronological age. In blue solid lines, patients with a Heart Age Gap ≤0; in green dotted lines, patients with a Heart Age Gap within 0 to 10 years; and in red dashed lines, patients with a Heart Age Gap exceeding 10 years. The log rank and P-values presented refer to the difference between Heart Age Gap >10 years (red, dashed lines) and the other two groups combined (blue solid lines and green dotted lines). Note that the survival graph above shows the survival in different heart gap groups. Although the unadjusted DNN-AI ECG Heart Age Gap shows no association with survival as depicted above, it is significantly associated with survival after adjusting for age (see text). A-ECG, advanced electrocardiography; DNN-AI, deep neural network artificial intelligence.

Figure 3

Scatter plots showing the relation between Heart Age (upper panels; left: A-ECG, right: DNN-AI) and Heart Age Gap (lower panels) and chronological age. The dashed lines denote the line of identity, i.e. no difference between Heart Age and chronological age. In most cases, in this clinical cohort with a high prevalence of disease, A-ECG Heart Age was higher than chronological age. DNN-AI ECG Heart Age Gap was lower in older patients than in younger (lower right panel), while an opposite trend could be observed for A-ECG Heart Age Gap.