Dataset Information

Diagnostic performance of electrocardiographic criteria in echocardiographic diagnosis of different patterns of left ventricular hypertrophy.

ABSTRACT:

Background

Electrocardiogram (ECG) is considered the initial screening method for the detection of left ventricular hypertrophy (LVH) despite its low sensitivity. However, there are no data on how ECG criteria for LVH perform in patients with concentric (cLVH) and eccentric LVH (eLVH).

Methods

In the setting of the Corinthia cross-sectional study, ECGs were analyzed in 1,570 participants of the study. Seven ECG LVH criteria were calculated (Sokolow-Lyon voltage, index, and product, sex-specific Cornell voltage and product, Lewis voltage, and the Framingham), whereas LVH was defined, based on echocardiographic data, as left ventricular mass indexed for body surface area (BSA) of at least 125 g/m² in men and at least 110 g/m² in women.

Results

Regarding the frequency encountered for each ECG LVH criterion, there was no difference between eLVH and cLVH. However, when ECG criteria were compared as continuous variables between LVH groups, Cornell voltage and product were higher in cLVH individuals, with a value of Cornell voltage >13.95 mV having 61% sensitivity and 62% specificity to differentiate cLVH from eLVH (p = .05). Even after adjustment for age, sex, body mass index, and hypertension, the occurrence of Cornell voltage or product increased the odds of cLVH by 1.6 times (p = .001).

Conclusion

Cornell voltage and product criteria disclosed a superior discriminative ability for the detection of LVH via ECG. When further categorizing LVH as concentric and eccentric, Cornell product depicted the higher discriminative ability for cLVH.

SUBMITTER: Oikonomou E

PROVIDER: S-EPMC7358819 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diagnostic performance of electrocardiographic criteria in echocardiographic diagnosis of different patterns of left ventricular hypertrophy.

Oikonomou Evangelos E Theofilis Panagiotis P Mpahara Aikaterini A Lazaros George G Niarchou Panagioula P Vogiatzi Georgia G Tsalamandris Sotirios S Fountoulakis Petros P Christoforatou Evangelia E Mystakidou Vasiliki V Anastasiou Maria M Goliopoulou Athina A Tousoulis Dimitris D

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc 20191114 3

<h4>Background</h4>Electrocardiogram (ECG) is considered the initial screening method for the detection of left ventricular hypertrophy (LVH) despite its low sensitivity. However, there are no data on how ECG criteria for LVH perform in patients with concentric (cLVH) and eccentric LVH (eLVH).<h4>Methods</h4>In the setting of the Corinthia cross-sectional study, ECGs were analyzed in 1,570 participants of the study. Seven ECG LVH criteria were calculated (Sokolow-Lyon voltage, index, and product ...[more]

PMID: 31724804

Similar Datasets

Project description: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.

Dataset Information

Diagnostic performance of electrocardiographic criteria in echocardiographic diagnosis of different patterns of left ventricular hypertrophy.

Background

Methods

Results

Conclusion

Publications

Diagnostic performance of electrocardiographic criteria in echocardiographic diagnosis of different patterns of left ventricular hypertrophy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets