Key result
A machine learning model using clinical and ECG features distinguished normal left ventricular geometry from concentric remodeling and hypertrophy with 86% accuracy and an AUC of 0.89.
Why the study?
Cardiac remodeling is an important aspect of cardiovascular disease progression, and innovative solutions are needed to improve early detection of abnormal left ventricular geometry before left ventricular hypertrophy in patients without established cardiovascular disease.
Does a machine learning model using clinical and ECG parameters accurately detect abnormal left ventricular geometry in patients without established cardiovascular disease?
Cross-Sectional (n=528)
Does a machine learning model using clinical and ECG parameters accurately detect abnormal left ventricular geometry in patients without established cardiovascular disease?
An ECG and clinical parameter-based machine learning model can accurately detect early abnormal left ventricular geometry, offering a potential tool for risk stratification in primary care.
ECG-clinical ML model may aid early LV geometry detection in primary care; hypothesis-generating and requires prospective validation before practice change.
Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied on basic clinical parameters and electrocardiographic features for detecting abnormal left ventricular geometry (LVG), even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. After careful screening, we enrolled 528 subjects with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). We trained nonlinear predictive ML models, to classify subjects with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow-Lyon voltage, QRS-T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from all others (CR+LVH), with accuracy 86%, specificity 75%, sensitivity 95%, and area under the receiver operating curve (AUC/ROC) 0.89. We also trained our model to classify NG and CR (NG+CR) against those with established LVH, with accuracy 89%, specificity 97%, sensitivity 50%, and AUC/ROC 0.85. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, especially in primary care settings, and ML may enable this direction.
No takes yet. Share an insight, caveat, or question.
Angelaki et al. (2020) conducted a cross-sectional in Without established cardiovascular disease (n=528). Machine learning model using clinical and ECG features was evaluated on Classification of normal geometry versus concentric remodeling and left ventricular hypertrophy (AUC 0.89). A machine learning model using clinical and ECG features distinguished normal left ventricular geometry from concentric remodeling and hypertrophy with 86% accuracy and an AUC of 0.89.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: