Abstract
The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard diagnostic tool to detect LVH and measured the ECG abnormalities found in Echo-LVH. We included 432 patients (power = 99%). Of these, 202 patients (46.7%) had Echo-LVH and 240 (55.6%) were males. We included a wide range of ventricular masses and Echo-LVH severities which were classified as mild (n = 77, 38.1%), moderate (n = 50, 24.7%) and severe (n = 75, 37.1%). Data was divided into a training/testing set (80%/20%) and we applied logistic regression analysis on the ECG measurements. The logistic regression model with the best ability to identify Echo-LVH was introduced into the C5.0 ML algorithm. We created multiple decision trees and selected the tree with the highest performance. The resultant five-level binary decision tree used only six predictive variables and had an accuracy of 71.4% (95%CI, 65.5–80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value of 66.6% and a negative predictive value of 69.3%. Internal validation reached a mean accuracy of 71.4% (64.4–78.5). Our results were reproduced in a second validation group and a similar diagnostic accuracy was obtained, 73.3% (95%CI, 65.5–80.2), sensitivity (81.6%), specificity (69.3%), positive predictive value (56.3%) and negative predictive value (88.6%). We calculated the Romhilt-Estes multilevel score and compared it to our model. The accuracy of the Romhilt-Estes system had an accuracy of 61.3% (CI95%, 56.5–65.9), a sensitivity of 23.2% and a specificity of 94.8% with similar results in the external validation group. In conclusion, the C5.0 ML algorithm surpassed the accuracy of current ECG criteria in the detection of Echo-LVH. Our new criteria hinge on ECG abnormalities that identify high-risk patients and provide some insight on electrogenesis in Echo-LVH.
Highlights
Since 1909, over thirty-six electrocardiographic left ventricular hypertrophy (ECG-LVH) criteria have been proposed, but most are redundant or oversimplify the electrical changes in LVH [1, 2]
A more realistic approach was developed by Romhilt-Estes in 1968, when they created a multilevel score system using a logistic regression model based on a broad spectrum of ECG abnormalities associated with ECG-LVH (i.e. QRS voltage, ST “strain” pattern, QRS duration), its sensitivity ( 30%) and accuracy ( 60%) are low [5, 6]
The performance with internal validation reached a diagnostic accuracy of 71.4%, (95%CI, 65.5– 80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value (PPV) of 66.6%, and negative predictive value (NPV) of 69.3%
Summary
Since 1909, over thirty-six electrocardiographic left ventricular hypertrophy (ECG-LVH) criteria have been proposed, but most are redundant or oversimplify the electrical changes in LVH [1, 2]. Most criteria (i.e. Cornell, Sokolov-Lyon) are based solely on increased QRS voltage, but this is not a consistent finding in all patients with ECG-LVH [1, 3, 4]. A more realistic approach was developed by Romhilt-Estes in 1968, when they created a multilevel score system using a logistic regression model based on a broad spectrum of ECG abnormalities associated with ECG-LVH (i.e. QRS voltage, ST “strain” pattern, QRS duration), its sensitivity ( 30%) and accuracy ( 60%) are low [5, 6]. In the 21st century almost everyone agrees that the ECGs role in Echo-LVH should provide a basic understanding of the electrical remodelsing inherent to hypertrophy [7]. A Machine Learning (ML) approach could be useful in these cases
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