Combined Effects of Age and Comorbidities on Electrocardiographic Parameters in a Large Non-Selected Population

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

Age and gender differences of basic electrocardiographic values and abnormalities in the general adult population; Tehran Cohort Study

BACKGROUND

Although several studies are available regarding baseline Electrocardiographic (ECG) parameters and major and minor ECG abnormalities, there is considerable controversy regarding their age and gender differences in the literature.

METHODS

Data from 7630 adults aged ≥ 35 from the Tehran Cohort Study registered between March 2016 and March 2019 were collected. Basic ECG parameters values and abnormalities related to arrhythmia, defined according to the American Heart Association definitions, were analyzed and compared between genders and four distinct age groups. The odds ratio of having any major ECG abnormality between men and women, stratified by age, was calculated.

RESULTS

The average age was 53.6 (± 12.66), and women made up 54.2% (n = 4132) of subjects. The average heart rate (HR) was higher among women(p < 0.0001), while the average values of QRS duration, P wave duration, and RR intervals were higher among men(p < 0.0001). Major ECG abnormalities were observed in 2.9% of the study population (right bundle branch block, left bundle branch block, and Atrial Fibrillation were the most common) and were more prevalent among men compared to women but without statistical significance (3.1% vs. 2.7% p = 0.188). Moreover, minor abnormalities were observed in 25.9% of the study population and again were more prevalent among men (36.4% vs. 17% p < 0.001). The prevalence of major ECG abnormalities was significantly higher in participants older than 65.

CONCLUSION

Major and minor ECG abnormalities were roughly more prevalent in male subjects. In both genders, the odds of having major ECG abnormalities surge with an increase in age.

Influence of Age on Hyperoxia-Induced Cardiac Pathophysiology in Type 1 Diabetes Mellitus (T1DM) Mouse Model

Mechanical ventilation often results in hyperoxia, a condition characterized by excess SpO2 levels (>96%). Hyperoxia results in changes in the physiological parameters, severe cardiac remodeling, arrhythmia development, and alteration of cardiac ion channels, all of which can point toward a gradual increase in the risk of developing cardiovascular disease (CVD). This study extends the analysis of our prior work in young Akita mice, which demonstrated that exposure to hyperoxia worsens cardiac outcomes in a type 1 diabetic murine model as compared to wild-type (WT) mice. Age is an independent risk factor, and when present with a major comorbidity, such as type 1 diabetes (T1D), it can further exacerbate cardiac outcomes. Thus, this research subjected aged T1D Akita mice to clinical hyperoxia and analyzed the cardiac outcomes. Overall, aged Akita mice (60 to 68 weeks) had preexisting cardiac challenges compared to young Akita mice. Aged mice were overweight, had an increased cardiac cross-sectional area, and showed prolonged QTc and JT intervals, which are proposed as major risk factors for CVD like intraventricular arrhythmias. Additionally, exposure to hyperoxia resulted in severe cardiac remodeling and a decrease in Kv 4.2 and KChIP2 cardiac potassium channels in these rodents. Based on sex-specific differences, aged male Akita mice had a higher risk of poor cardiac outcomes than aged females. Aged male Akita mice had prolonged RR, QTc, and JT intervals even at baseline normoxic exposure. Moreover, they were not protected against hyperoxic stress through adaptive cardiac hypertrophy, which, at least to some extent, is due to reduced cardiac androgen receptors. This study in aged Akita mice aims to draw attention to the clinically important yet understudied subject of the effect of hyperoxia on cardiac parameters in the presence of preexisting comorbidities. The findings would help revise the provision of care for older T1D patients admitted to ICUs.

The Relationship between the Frontal QRS-T Angle on ECG and Physical Activity Level in Young Adults

BACKGROUND

The heart's electrical activity has been the subject of numerous research concerning various physiological parameters. The frontal QRS-T angle (FQRST) is an advanced ECG variable with clinical epidemiological utility. This study aimed to determine the relationship between FQRST and physical activity exposure among young adults.

METHODS

We recorded the ECG with 12 leads of 124 participants (mean age 20.28 ± 2.23 years, age range 18-27 years). Next, we measured their physical activity level (PAL) with the International Physical Activity Questionnaire-Short Form (IPAQ), which categorizes activity into three classes: low, moderate, or high.

RESULTS

An inferential analysis, based on the Kruskal-Wallis H test and Mann-Whitney U test, revealed a statistically significant difference in FQRST between the three groups of subjects, classified by their PAL (p < 0.001). We also identified a significant regression model between the body mass index (BMI) and the FQRST (p < 0.001).

CONCLUSIONS

The physical activity regime of young adults significantly influences the concordance between ventricular depolarization and repolarization, reflected in the FQRST's width. Also, we found a regression model between FQRST and BMI with statistical significance.