Key result
ECG-based machine learning outperforms traditional QTc for predicting LVDD, achieving ~84% AUC in young men.
Why the study?
ECG criteria and machine learning for ECG features have been applied to predict LVDD in middle- and older-aged individuals, but performance in young adults remains unclear.
Do machine learning classifiers using ECG and biological features improve the prediction of left ventricular diastolic dysfunction compared to the traditional QTc interval in young Asian male adults?
Observational (n=2,206)
No
Do machine learning classifiers using ECG and biological features improve the prediction of left ventricular diastolic dysfunction compared to the traditional QTc interval in young Asian male adults?
Machine learning models using standard 12-lead ECG features can reliably predict left ventricular diastolic dysfunction in young Asian male adults, significantly outperforming traditional QTc interval criteria.
Answers are grounded in this paper and cite their sources.
May support ECG machine learning for diastolic dysfunction screening in young Asian men; hypothesis-generating and requires prospective validation.
Left ventricular diastolic dysfunction (LVDD) occurs at the initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features have been applied to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. Three machine learning classifiers including random forest (RF), support vector machine (SVM) and gradient boosting decision tree (GBDT) for the input of 26 ECG features with or without 6 other biological features (age, anthropometrics and blood pressures) are compared with the corrected QT (QTc) interval, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the following echocardiographic criteria: (1) an E/A ratio of mitral inflow <; 0.8; (2) a lateral mitral annulus velocity e' <; 10 cm/s; and (3) an E/e' ratio >14. The best areas under the receiver operating characteristic curve were observed in machine learning of the RF for ECG only (84.1%) and of the SVM for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%). If the specificity is chosen to be approximately 75.0%, the sensitivity of the RF for ECG only reaches 81.0% and that of the SVM for all ECG and biological features is raised to 85.7%, both of which are higher than 47.6% by the QTc interval. This study suggests that using machine learning for ECG features only or with other biological features to predict LVDD in young Asian adults is reliable. The proposed methods provide for the early detection of LVDD for young adults and are helpful for taking preventive action on heart failure.
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Lin et al. (2021) conducted an observational in Left Ventricular Diastolic Dysfunction (n=2,206). Machine learning classifiers (Random Forest, SVM, GBDT) vs. Corrected QT (QTc) interval was evaluated on Area under the receiver operating characteristic curve (AUC) for predicting LVDD. Random forest machine learning using only electrocardiographic features achieved an area under the curve of 84.1%, outperforming the traditional QTc interval (64.6%) for predicting left ventricular diastolic dysfunction in young Asian male adults.
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