Introduction
Surface electrocardiograms (ECGs) are used frequently in routine
clinical care, but also in investigational studies examining the effects
of pharmacological and non-pharmacological treatments on the heart.
Readout measures include the RR interval, PR interval, QRS duration and
(corrected) QT interval. Typically, the pharmacological treatment
effects are mediated by recognized channels on the cardiac surface.
[1]However, there are cardiac effects that require a longer period
of time to become visible on the surface ECG, such as aging induced
cardiac fibrosis, and it is largely unknown if these subtle effects can
be visualized on a surface ECG. [2, 3]
There has been a number of recent investigations regarding the
prediction of physiological age using medical records, vital signs and
laboratory data, or epigenetic changes. [4-6] These investigations
indicated the existence of a gap between predicted physiological age and
actual chronological age. Exploration of this gap is clinically
important as a serious gap difference has been shown to be associated
with higher risks of all-cause mortality, cardiovascular disease,
obesity, earlier menopause, and frailty. [5, 7-11] Various previous
studies have already shown that the 12-lead ECG can be a reliable tool
to estimate physiological aging. [5, 7-16]
Previous studies have applied artificial intelligence to the raw ECG
data, allowing estimation of physiologic ECG age, which was found to
reflect aging and comorbidities. [17] However, these algorithms were
based on large hospital datasets, thus including patients that may have
disease-induced abnormalities in their ECGs, which makes the outcome
difficult to interpret when applied to a healthy volunteer. Therefore,
the aim of the present analysis was to develop a neural network in
healthy volunteers to characterize the effect of aging on the ECG.