R peak time prolongation and R peak delay in leads I, V5, or V6

Recke, Siegfried H.; Gansser, Rolf; Marienhagen, Jörg; Platsch, Günther; Feistel, H.; Weniger, J; Emde, J. von der

doi:10.1016/s0022-0736(05)80095-0

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Delayed time to ID is thought to represent conduction delay secondary to increases in left ventricular cavity size and increases in left ventricular end‐diastolic volume 31, 32. Similarly, abnormalities of left ventricular repolarization possibly detect structural abnormalities that predispose to HFrEF rather than HFpEF 29, 33, 34.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi‐Ethnic Study of Atherosclerosis

O’Neal

Mazur

Bertoni

et al. 2017

JAHA

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BackgroundSeveral markers detected on the routine 12‐lead ECG are associated with future heart failure events. We examined whether these markers are able to separate the risk of heart failure with reduced ejection fraction (HFrEF) from heart failure with preserved ejection fraction (HFpEF).Methods and ResultsWe analyzed data of 6664 participants (53% female; mean age 62±10 years) from MESA (Multi‐Ethnic Study of Atherosclerosis) who were free of cardiovascular disease at baseline (2000–2002). A competing risks analysis was used to compare the association of several baseline ECG predictors with HFrEF and HFpEF detected during a median follow‐up of 12.1 years. A total of 127 HFrEF and 117 HFpEF events were detected during follow‐up. In a multivariable adjusted model, prolonged QRS duration, delayed intrinsicoid deflection, left‐axis deviation, right‐axis deviation, prolonged QT interval, abnormal QRS‐T axis, left ventricular hypertrophy, ST/T‐wave abnormalities, and left bundle‐branch block were associated with HFrEF. In contrast, higher resting heart rate, abnormal P‐wave axis, and abnormal QRS‐T axis were associated with HFpEF. The risk of HFrEF versus HFpEF was significantly differently for delayed intrinsicoid deflection (hazard ratio: 4.90 [95% confidence interval (CI), 2.77–8.68] versus 0.94 [95% CI, 0.29–2.97]; comparison P=0.013), prolonged QT interval (hazard ratio: 2.39 [95% CI, 1.55–3.68] versus 0.52 [95% CI, 0.23–1.19]; comparison P<0.001), and ST/T‐wave abnormalities (hazard ratio: 2.47 [95% CI, 1.69–3.62] versus 1.13 [95% CI, 0.72–1.77]; comparison P=0.0093).ConclusionsMarkers of ventricular repolarization and delayed ventricular activation are able to distinguish between the future risk of HFrEF and HFpEF. These findings suggest a role for ECG markers in the personalized risk assessment of heart failure subtypes.

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Section: Discussionmentioning

confidence: 99%

Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi‐Ethnic Study of Atherosclerosis

O’Neal

Mazur

Bertoni

et al. 2017

JAHA

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“…This is supported by a recent examination of 146 adults with aortic insufficiency that found delayed ID to be highly predictive of a reduced ejection fraction (<50%) with a specificity of 89.1% (16). Additionally, delayed ID has been shown to suggest volume overload (e.g., increased left ventricular end-diastolic volume) in patients with chronic mitral regurgitation (17). These findings linking delayed ID with left ventricular dysfunction suggest that time to ID is able to detect subclinical anatomical abnormalities that predispose to heart failure.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic Time to Intrinsicoid Deflection and Heart Failure: The Multi‐Ethnic Study of Atherosclerosis

O’Neal

Qureshi

Nazarian

et al. 2016

Clinical Cardiology

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Background Time to intrinsicoid deflection (ID), the time from onset of the QRS complex to the peak of the R wave on the electrocardiogram, represents delayed ventricular activation and suggests that impaired myocardial function is present. It is unknown if delayed time to ID is predictive of future heart failure events. Hypothesis We hypothesized delayed ID is predictive of future heart failure events. Methods A total of 6,394 (mean age 62 ± 10 years; 54% women; 38% whites; 28% blacks; 22% Hispanics; 12% Chinese-Americans) participants without clinically apparent cardiovascular disease or major ventricular conduction delay (QRS ≥120 ms) from the Multi-Ethnic Study of Atherosclerosis were included. Time to ID was automatically measured from baseline electrocardiograms (2000–2002) as the maximum value in leads V5 and V6. Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (95%CI) for the association between time to ID and heart failure. Results Over a median follow-up of 11.2 years, a total of 217 (3.4%) participants developed heart failure (incidence rate per 1000 person-years=3.33, 95%CI=2.91, 3.80). In a multivariable Cox regression analysis adjusted for demographics, cardiovascular risk factors, and potential confounders, each 10 ms increase in maximum time to ID was associated with an increased risk for heart failure (HR=1.42, 95%CI=1.15, 1.74). The results remained similar when stratified by age, sex, and race/ethnicity. Conclusion Delayed ID is able to identify individuals at risk for developing heart failure before major ventricular conduction delays (e.g., bundle branch block) are evident.

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The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms

et al. 2021

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Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

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R peak time prolongation and R peak delay in leads I, V5, or V6

Cited by 3 publications

References 9 publications

Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi‐Ethnic Study of Atherosclerosis

Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi‐Ethnic Study of Atherosclerosis

Electrocardiographic Time to Intrinsicoid Deflection and Heart Failure: The Multi‐Ethnic Study of Atherosclerosis

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms

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