Comparisons of traditional electrocardiographic criteria for left and right ventricular hypertrophy in young Asian women: The CHIEF heart study

Electrocardiographic and echocardiographic predictors of greater carotid intima-media thickness in tactical athletes: The CHIEF atherosclerosis study

OBJECTIVE

Both electrocardiographic and echocardiographic left ventricular hypertrophy (LVH) have been reported with an association with greater carotid intima-media thickness (cIMT), a marker of subclinical atherosclerosis in patients with hypertension, while the associations are unclear in physically fit young adults.

METHODS

A total of 1822 Taiwanese military personnel, aged 18-40 years, received an annual health examination including electrocardiography (ECG) and echocardiography in 2018-2020. Left carotid bulb cIMT was measured by high-resolution ultrasonography. Multiple logistic regression analysis with adjustments for age, sex, smoking, alcohol consumption, body mass index, mean blood pressure, and physical fitness was used to determine the associations between echocardiographic and ECG parameters and the highest quintile of cIMT (≥0.8 mm).

RESULTS

Cornell-based LVH, Myers et al.-based RVH and heart rate ≥75/min were associated with cIMT ≥0.8 mm [odds ratios (ORs) and 95% confidence intervals: 1.54 (1.01, 2.35), 1.66 (1.18, 2.33), and 1.39 (1.06, 1.83), respectively], while echocardiographic LVH defined as ≥46.0 g/m^2.7 for men and ≥38.0 g/m^2.7 for women was inversely associated with cIMT ≥0.8 mm [OR: 0.45 (0.24, 0.86)].

CONCLUSION

In tactical athletes of military, the associations of ECG and echocardiographic LVH with cIMT were in opposite directions. Higher physical fitness may cause cardiac muscle hypertrophy and reduce the atherosclerosis severity, possibly leading to the paradoxical echocardiographic finding. This study suggests that ECG-based LVH remains a good marker of subclinical atherosclerosis in our military population.

Sex-specific cardiometabolic risk markers of left ventricular mass in physically active young adults: the CHIEF heart study

Greater physical fitness may lead to greater left ventricular mass (LVM) and reduce the effect of cardiometabolic risk factors on LVM. However, the cardiometabolic biomarkers associations for LVM have not been clarified in physically active young adults. This study included 2019 men and 253 women, aged 18-43 years, from the military in Taiwan. All participants underwent anthropometric and blood metabolic markers measurements, and completed a 3000-m run test for assessing fitness. LVM was calculated on the basis of an echocardiography. Multiple linear regression was used to determine the sex-specific associations between cardiometabolic risk markers and LVM indexed for the body height (g/m^2.7). In men, age, systolic blood pressure (SBP), 3000-m running time, serum triglycerides, serum uric acid and waist circumference (WC) were correlated with LVM index (β = 0.07, 0.10, - 0.01, 0.01, 0.24 and 0.24, respectively; all p-values < 0.05). The correlations were not significant for fasting plasma glucose, total cholesterol and high-density lipoprotein cholesterol (HDL-C). In women, SBP, HDL-C and WC were correlated with LVM index in the univariate analysis (β = 0.07, - 0.05 and 0.32, respectively; all p-values < 0.05), whereas the correlation was only significant for WC in the multiple linear regression analysis (β = 0.20; p-value < 0.001). In physically active adults, the associations of cardiometabolic risk markers with LVM might vary by sex. Better endurance exercise performance associated with greater LVM was noted only in men, while greater WC was the only metabolic risk marker for greater LVM in both men and women.

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

Background

Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.

Methods

In a sample of 2,206 male adults aged 17–43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.

Results

The greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70–75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.

Conclusions

This study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.