Heart age estimated using explainable advanced electrocardiography

Abstract

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-min ECG Heart Age. Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict patients’ Bayesian 5-min ECG Heart Ages from their standard, resting 10-s 12-lead ECGs. The difference between 5-min and 10-s ECG Heart Ages were analyzed, as were the differences between 10-s ECG Heart Age and the chronological age (the Heart Age Gap). In total, 2,771 subjects were included (n = 1682 healthy volunteers, n = 305 with cardiovascular risk factors, n = 784 with cardiovascular disease). Overall, 10-s Heart Age showed strong agreement with the 5-min Heart Age (R2 = 0.94, p < 0.001, mean ± SD bias 0.0 ± 5.1 years). The Heart Age Gap was 0.0 ± 5.7 years in healthy individuals, 7.4 ± 7.3 years in subjects with cardiovascular risk factors (p < 0.001), and 14.3 ± 9.2 years in patients with cardiovascular disease (p < 0.001). Heart Age can be accurately estimated from a 10-s 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without deep neural network-type artificial intelligence techniques. The Heart Age Gap increases markedly with cardiovascular risk and disease.

Diagnosing diastolic dysfunction using explainable advanced electrocardiography

“…The study excluded patients with LVEF <55%. A-ECG scores previously validated for the detection of reduced LVEF were also applied to this cohort, confirming a low likelihood of left ventricular systolic dysfunction for both the control and diastolic dysfunction cohorts (7 [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] % and 13 [4-33] %, respectively), where a probability of >50% was considered a high likelihood.…”

“…A-ECG analysis on standard 10-sec ECGs enables improved diagnostic performance by incorporating not only conventional ECG measurements such as waveform amplitudes, axes and durations, but also measures of derived three-dimensional vectorcardiography (VCG) and waveform complexity via singular value decomposition. [10][11][12] . A-ECG has been able to discriminate between health and disease in several conditions such as coronary artery disease, cardiomyopathy, LV hypertrophy and LV systolic dysfunction, with notably higher diagnostic accuracy compared to conventional ECG 10,13,14 .…”

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes

“…Recent studies have indicated that ECG analyses utilizing deep learning approaches to AI-based ECG may predict mortality, cardiac arrhythmias, cardiac function, heart failure, valvular heart disease, and electrolyte abnormalities ( 7 – 12 , 35 , 36 ). By comparing the ECG age calculated via deep neural networks to the CA, new information on mortality and CVD risk factors has recently been derived ( 8 , 13 , 36 ).…”

“…Many risk factors for CVD could be reduced by lifestyle modifications such as quitting smoking, changing one's diet, and increasing one's physical activity. The format of the heart age presentation could help patients understand and motivate them regarding their CVD risk ( 35 ). Patients changing their lifestyles can contribute to patient-centered care that positively affects the outcome of long-term CVD.…”

An electrocardiography score predicts heart failure hospitalization or death beyond that of cardiovascular magnetic resonance imaging

“…Resting 12-lead ECG data for each subject was collected from the local ECG storage system (MUSE® Cardiology Information System, Version 8.0 SP2, GE Healthcare, IL, USA) and exported into anonymized xml files with coded identification. The xml files were analyzed digitally using previously described semi-automated software developed in-house 8 , 12 . The following ECG measures were included into the ECG prognosis score analysis: conventional ECG parameters including scalar durations, axes, and amplitudes; vectorcardiographic measures derived using Kors’ transform 13 including spatial durations, axes, and amplitudes; and QRS-wave and T-wave complexity measures quantified using singular value decomposition 8 .…”