Diagnostic utility of the electrocardiographic left ventricular hypertrophy criteria in specific populations.

BACKGROUND: Several electrocardiographic (ECG) criteria have been validated for the diagnosis of left ventricular hypertrophy (LVH); the majority in Caucasian subjects from Europe and North America. Diagnostic utility of ECG criteria to detect LVH has never been established in our population; nonetheless they are frequently used. OBJECTIVE: To evaluate the diagnostic utility of different LVH ECG criteria in a Northern Mexican population and to determine the effect of gender, age, body mass index (BMI), hypertension and ischaemic heart disease (IHD) on their performance. METHODS: We conducted an observational, case-control study in patients divided according to the presence of LVH in an echocardiogram (Echo). We calculated the accuracy, sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of 22 ECG criteria. RESULTS: Four hundred thirty-two patients were studied (202 had LVH). The Dalfó criterion (ECG18. SV3 + RaVL) had the best diagnostic performance with a Se of 56%, Sp of 71.3%, PPV 62.9%, NPV 65% and a diagnostic accuracy (95%CI) of 64.1% (59.5-68.6). This criterion had the highest accuracy in both genders, in all BMI, in older patients (>60 years) and in those with positive Echo ischaemic heart disease (IHD); it also performed well in patients with history of IHD and in hypertensive patients. VDP Cornell had the best accuracy in patients less than 60 years old, and in patients with non-ischaemic findings by Echo. CONCLUSIONS: The Dalfó criteria had the overall best accuracy in the detection of LVH, and specific populations.

Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach.

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.