Computer simulation of ECG manifestations of left ventricular electrical remodeling

Bachárová, Ljuba; Szathmary, Vavrinec; Potse, Mark; Mateášik, Anton

doi:10.1016/j.jelectrocard.2012.07.009

Cited by 17 publications

(12 citation statements)

References 36 publications

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“…However QRS changes in LVH can also include prolonged QRS, left axis deviation, left anterior fascicular block, and left bundle block-like patterns 25 . While it had previously been believed that changes in the QRS complex were associated with an increased LVM, studies have shown that the changes are due to a combination of anatomical and electrical remodeling 26 .…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic Left Ventricular Hypertrophy as a Predictor of Cardiovascular Disease Independent of Left Ventricular Anatomy in Subjects Aged ≥65 Years

Leigh

O’Neal

Soliman

2016

The American Journal of Cardiology

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Left ventricular hypertrophy (LVH) diagnosed by electrocardiography (ECG-LVH) and echocardiography (echo-LVH) are independently associated with an increased risk of cardiovascular disease (CVD) events. However, it is unknown if ECG-LVH retains its predictive properties independent of left ventricular anatomy. We compared the risk of CVD associated with ECG-LVH and echo-LVH in 4,076 participants (41% male, 86% white) from the Cardiovascular Health Study (CHS), who were free of baseline CVD. ECG-LVH was defined with Minnesota ECG Classification criteria from baseline ECG data. Echo-LVH was defined by sex-specific left ventricular mass values normalized to body surface area (male: >102 g/m2; female: >88 g/m2). ECG-LVH was detected in 144 (3.5%) participants and echo-LVH in 430 (11%) participants. Over a median follow-up of 10.6 years, 2,274 CVD events occurred. In a multivariable Cox regression analysis adjusted for common CVD risk factors, ECG-LVH (HR=1.84, 95%CI=1.51, 2.24) and echo-LVH (HR=1.35, 95%CI=1.19, 1.54) were associated with an increased risk for CVD events. The association between ECG-LVH and CVD events was not substantively altered with further adjustment for echo-LVH (HR=1.76, 95%CI=1.45, 2.15). In conclusion, the association of ECG-LVH with CVD events is not dependent on echo-LVH. This finding provides support to the concept that ECG-LVH is an electrophysiologic marker with predictive properties independent of left ventricular anatomy.

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Section: Discussionmentioning

confidence: 99%

Electrocardiographic Left Ventricular Hypertrophy as a Predictor of Cardiovascular Disease Independent of Left Ventricular Anatomy in Subjects Aged ≥65 Years

Leigh

O’Neal

Soliman

2016

The American Journal of Cardiology

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“…The QRS changes that occur with ECG-LVH have been shown to represent a combination of anatomic and electric remodeling [24, 25]. In contrast, echo-LVH commonly relies entirely on left ventricular mass [26].…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic and Echocardiographic Left Ventricular Hypertrophy in the Prediction of Stroke in the Elderly

O’Neal

Almahmoud

Qureshi

et al. 2015

Journal of Stroke and Cerebrovascular Diseases

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Introduction It is unclear whether LVH detected by electrocardiogram (ECG-LVH) is equally predictive of heart failure as LVH detected by echocardiography (echo-LVH). Methods This analysis included 4,008 white participants (41% male) aged 65 years or older from the Cardiovascular Health Study (CHS) who were free of stroke and major intraventricular conduction defects at baseline. ECG-LVH was defined by the Cornell criteria from baseline ECG data and echo-LVH was computed from baseline echocardiography measurements. Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (CI) for the association between ECG-LVH and echo-LVH and adjudicated incident stroke events, separately. Harrell’s concordance indices (C-index) were calculated for the Framingham Stroke Risk Score with inclusion of ECG-LVH and echo-LVH, separately. Results ECG-LVH was detected in 136 (3.4%) participants and echo-LVH was present in 208 (5.2%) participants. Over a median follow-up of 13 years (interquartile range=7.5, 19.1), a total of 769 (19%; incidence rate=15.4 per 1000 person-years) strokes occurred. In a multivariable Cox regression analysis adjusted for stroke risk factors and potential confounders, ECG-LVH (HR=1.68, 95%CI=1.23, 2.28) and echo-LVH (HR=1.58, 95%CI=1.17, 2.14) were associated with an increased risk of stroke. Similar values were obtained for the C-index when either ECG-LVH (C-index=0.786) or echo-LVH (C-index=0.786) were included in the Framingham Stroke Risk Score. Conclusion ECG-LVH and echo-LVH can be used interchangeably in stroke risk scores.

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“…They show the effect of simulated myocardial infarctions at various locations on ECG changes, specifically in the ST segment. Another study by Bacharova et al [85] used computer simulations to investigate the influence of left ventricular (LV) mass on QRS, and specifically increased QRS amplitude, in the context of LV hypertrophy. In a more recent work, Bacharova et al [86] investigated the effect of slow ventricular activation on the QRS complex and showed how alteration of electrical properties may mimic ECG morphologies associated with anatomical abnormalities.…”

Section: Ecg Computer Simulationsmentioning

confidence: 99%

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

179

102

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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Computer simulation of ECG manifestations of left ventricular electrical remodeling

Cited by 17 publications

References 36 publications

Electrocardiographic Left Ventricular Hypertrophy as a Predictor of Cardiovascular Disease Independent of Left Ventricular Anatomy in Subjects Aged ≥65 Years

Electrocardiographic Left Ventricular Hypertrophy as a Predictor of Cardiovascular Disease Independent of Left Ventricular Anatomy in Subjects Aged ≥65 Years

Electrocardiographic and Echocardiographic Left Ventricular Hypertrophy in the Prediction of Stroke in the Elderly

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

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