Left ventricular hypertrophy: The relationship between the electrocardiogram and cardiovascular magnetic resonance imaging

Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis

Several studies suggested the utility of artificial intelligence (AI) in screening left ventricular hypertrophy (LVH). We hence conducted systematic review and meta-analysis comparing diagnostic accuracy of AI to Sokolow-Lyon's and Cornell's criteria. Our aim was to provide a comprehensive overview of the newly developed AI tools for diagnosing LVH. We searched MEDLINE, EMBASE, and Cochrane databases for relevant studies until May 2023. Included were observational studies evaluating AI's accuracy in LVH detection. The area under the receiver operating characteristic curves (ROC) and pooled sensitivities and specificities assessed AI's performance against standard criteria. A total of 66,479 participants, with and without LVH, were included. Use of AI was associated with improved diagnostic accuracy with summary ROC (SROC) of 0.87. Sokolow-Lyon's and Cornell's criteria had lower accuracy (0.68 and 0.60). AI had sensitivity and specificity of 69% and 87%. In comparison, Sokolow-Lyon's specificity was 92% with a sensitivity of 25%, while Cornell's specificity was 94% with a sensitivity of 19%. This indicating its superior diagnostic accuracy of AI based algorithm in LVH detection. Our study demonstrates that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity. These findings contribute to the validation of AI as a promising tool for LVH detection.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

BACKGROUND AND OBJECTIVE

Left ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN).

METHODS

Utilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis.

RESULTS

For LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807).

CONCLUSIONS

This study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

Estimating Left Ventricular Mass from the Electrocardiogram across the Spectrum of LV Mass from Normal to Increased LV Mass in an Older Age Group

Objectives

To examine the relationship of QRS voltages and left ventricular (LV) mass across the spectrum of individuals with different LV mass.

Methods

Twenty QRS voltage measurements or combinations were determined in a consecutive series of 159 adults with an ECG and echocardiogram without previous myocardial infarction, left or right bundle branch block, pre-excitation, or electronic pacemaker.

Results

The four strongest and significant correlations between QRS and LV mass were S in V4, deepest S wave in any precordial lead plus S in V4, S in V3, and S in V3 plus R in AVL times QRS duration. For men, the strength of the relationships were S in V3 (F = 33.8), deepest S wave in any precordial lead plus S V4 (F = 33.7), S in V3 plus R aVL (F = 29.9), S in V4 (F = 29.79), and deepest S in precordial leads (F = 17.9). The R wave in AVL alone did not correlate with LV mass. Criteria using the R wave in lateral precordial leads did not correlate as strongly with LV mass. For women, only S in V4 significantly correlated with LV mass. Overall, the R wave voltage in limb leads (AVL I or II) did not correlate with precordial S wave amplitudes. Univariate and multivariate analysis showed that some but not all QRS voltages correlated with each other. In multivariate analysis, using only single variables and not combination of QRS variables, the only significant relationship between QRS voltage and left ventricular mass was for men the S in V3 (p = 0.04) and for women S in V4 (p = 0.016) and R in V6 (p = 0.04).

Conclusion

The S wave in V3 and V4 correlate most strongly with LV mass while the R wave in limb leads, including AVL, do not correlate.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence

OBJECTIVE

Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).

METHODS

We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities.

RESULTS

Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality.

CONCLUSION

Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Improved evaluation of left ventricular hypertrophy using the spatial QRS-T angle by electrocardiography

Electrocardiographic (ECG) signs of left ventricular hypertrophy (LVH) lack sensitivity. The aim was to identify LVH based on an abnormal spatial peaks QRS-T angle, evaluate its diagnostic performance compared to conventional ECG criteria for LVH, and its prognostic performance. This was an observational study with four cohorts with a QRS duration < 120 ms. Based on healthy volunteers (n = 921), an abnormal spatial peaks QRS-T angle was defined as ≥ 40° for females and ≥ 55° for males. In other healthy volunteers (n = 461), the specificity of the QRS-T angle to detect LVH was 96% (females) and 98% (males). In patients with at least moderate LVH by cardiac imaging (n = 225), the QRS-T angle had a higher sensitivity than conventional ECG criteria (93–97% vs 13–56%, p < 0.001 for all). In clinical consecutive patients (n = 783), of those who did not have any LVH, 238/556 (43%) had an abnormal QRS-T angle. There was an association with hospitalization for heart failure or all-cause death in univariable and multivariable analysis. An abnormal QRS-T angle rarely occurred in healthy volunteers, was a mainstay of moderate or greater LVH, was common in clinical patients without LVH but with cardiac co-morbidities, and associated with outcomes.

Deep learning assessment of left ventricular hypertrophy based on electrocardiogram

Background

Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.

Methods

We built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).

Results

The LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).

Conclusion

Our study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

Implication of Different ECG Left Ventricular Hypertrophy in Patients Undergoing Transcatheter Aortic Valve Replacement

Background

Various ECG criteria for left ventricular hypertrophy (LVH) have been proposed, but their association with clinical outcomes in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement is unknown. We investigated the prevalence of ECG LVH according to different criteria and its prognostic impact on clinical outcomes after transcatheter aortic valve replacement.

Methods and Results

In this prospective observational cohort, we evaluated 700 patients who underwent transcatheter aortic valve replacement between March 2010 and December 2019. Baseline preprocedural LVH was defined by 3 ECG criteria—Sokolow‐Lyon, Romhilt‐Estes, and Cornell voltage criteria. The primary outcome was major adverse cardiac or cerebrovascular event (MACCE; composite of death, myocardial infarction, stroke, or rehospitalization from cardiovascular cause); the key secondary outcome was all‐cause and cardiovascular mortality. Among 596 eligible patients, the prevalence of LVH was determined as 56.3% by Sokolow‐Lyon, 31.1% by Romhilt‐Estes, and 48.1% by Cornell criteria. Regardless of the criteria, patients with ECG LVH had more severe aortic stenosis hemodynamics and higher left ventricular mass index. After multivariate adjustment, the presence of LVH by the Cornell criteria was significantly associated with lower risks of MACCE (adjusted hazard ratio [HR], 0.68; 95% CI, 0.51–0.91; P=0.009), all‐cause mortality (adjusted HR, 0.55; 95% CI, 0.34–0.90 [P=0.017]), and cardiovascular mortality (adjusted HR, 0.40; 95% CI, 0.20–0.79 [P=0.008]). However, this association was absent with the Sokolow‐Lyon and Romhilt‐Estes criteria.

Conclusions

ECG LVH by Cornell criteria only was significantly associated with lower risks of MACCE and all‐cause or cardiovascular mortality.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03298178.

Exposure to secondhand smoke is associated with increased left ventricular mass

INTRODUCTION

Chronic hypertension is associated with left ventricular hypertrophy. Recent evidence suggests that secondhand smoke (SHS) exposure is associated with chronic hypertension, so we sought to examine the relationship between secondhand smoke exposure and electrocardiographic left ventricular (LV) mass among non-smokers.

METHODS

This analysis included 4982 non-smoker participants from the Third National Health and Nutrition Examination (NHANES-III). Non-smoking was defined by self-report and serum cotinine ≤10 ng/mL, a biomarker for tobacco exposure. SHS exposure was defined as serum cotinine level ≥1 ng/mL. LV mass was estimated using an electrocardiographic model developed and applied in NHANES-III then validated in the Cardiovascular Health Study. Multivariable linear regression was used to examine the cross-sectional association between SHS exposure (vs no exposure) with estimated LV mass index. In similar models, we also examined the associations of LV mass index across quartiles of serum cotinine (reference group, 1st quartile) and in subgroups stratified by age, race, sex, hypertension, and obesity.

RESULTS

About 9.8% (n=489) of the participants were exposed to SHS. Exposure to SHS was associated with an estimated 2.9 g/m² increase in LV mass index, with a dose-response relationship between higher serum cotinine and LV mass index. These results were consistent in men and women, Whites and non-Whites, elderly and non-elderly, and those with and without hypertension. Significant effect modification was present among obese individuals with an estimated 4.8 g/m² increase in LV mass index (interaction p=0.01).

CONCLUSIONS

In a racially diverse sample of non-smokers, SHS is associated with increased LV mass with a dose-response relationship between level of exposure and LV mass. Effect modification was present among obese individuals. These findings underscore the harmful effect of passive smoking on the cardiovascular system and highlight the need for more restrictions on smoking in public areas, especially in countries or regions with less-stringent public health policies.

SD + SV4 diagnosis of left ventricular hypertrophy, a revaluation of ECG criterion by cardiac magnetic resonance imaging

Backgroud

Present electrocardiogram (ECG) criteria for diagnosing left ventricular hypertrophy (LVH) usually have low sensitivity, while the newly proposed SD + SV4 criterion, namely the deepest S‐wave amplitude in any lead (SD) plus SV4 amplitude, has been reported to have higher sensitivity and accuracy compared with other existing criteria. We aimed to further evaluate the diagnostic value of the SD + SV4 criterion in reference to the gold standard cardiac magnetic resonance imaging (CMR) in LVH diagnosis.

Methods

This retrospective study enrolled 138 patients who received CMR examination—60 patients with reduced ejection fraction (EF) and 78 patients with preserved EF. The left ventricular mass index (LVMI) measured by CMR was used as the gold standard for diagnosing LVH.

Result

The diagnostic value of the SD + SV4 criterion was compared with other 4 commonly used criteria. By CMR, 29 out of 138 people (21%) were diagnosed with LVH in reference to CMR. The SD + SV4 criterion had markedly higher sensitivity in diagnosing LVH compared with other criteria, but no higher specificity. There was no significant difference in area under receiver operating characteristic (ROC) curve among these criteria. The SD + SV4 criterion was not markedly consistent with CMR in diagnosing LVH. Compared to the other criteria, the SD + SV4 criterion had the highest sensitivity in patients with reduced ejection fraction; however, the area under the curve (AUC) of the SD + SV4 criterion in patients with reduced EF was significantly lower than in patients with preserved EF.

Conclusion

The newly proposed SD + SV4 criterion did not have a better diagnostic value compared with other existing criteria, and the statistical power of the SD + SV4 criterion was influenced by EF.

Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis

Background

Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases. Electrocardiogram readings have been reported to be affected by aging and, therefore, can predict patient mortality.

Methods

A total of 12,837 patients without structural heart disease who underwent electrocardiography at baseline were identified in the Shinken Database among those registered between 2010 and 2017 (n = 19,170). Using 438 electrocardiography parameters, predictive models for all-cause death and cardiovascular (CV) death were developed by a support vector machine (SVM) algorithm.

Results

During the observation period of 320.4 days, 55 all-cause deaths and 23 CV deaths were observed. In the SVM prediction model, the mean c-statistics of 10 cross-validation models with training and testing datasets were 0.881 ± 0.027 and 0.927 ± 0.101, respectively, for all-cause death and 0.862 ± 0.029 and 0.897 ± 0.069, respectively for CV death. For both all-cause and CV death, high values of permutation importance in the ECG parameters were concentrated in the QRS complex and ST-T segment.

Conclusions

Parameters acquired from 12-lead resting electrocardiography could be applied to predict the all-cause and CV deaths of patients without structural heart disease. The ECG parameters that greatly contributed to the prediction were concentrated in the QRS complex and ST-T segment.

Peguero-Lo Presti criteria for the diagnosis of left ventricular hypertrophy: A systematic review and meta-analysis

Background

The Peguero-Lo Presti criteria are novel electrocardiographic (ECG) diagnostic criteria for the detection of left ventricular hypertrophy (LVH) and represent the sum of the amplitude of the deepest S wave in any lead with the S wave in lead V₄ (S_D+SV₄). The diagnostic efficacy of the Peguero-Lo Presti criteria in LVH is still debatable. We aimed to test the sensitivity and specificity of the Peguero-Lo Presti criteria and compared them with those of the Cornell voltage index to assess their overall performance in LVH diagnosis.

Methods

Electronic databases (e.g., Medline, Web of Knowledge, Embase, and the Cochrane Library) were searched from their inception until May 18, 2020. Trials written in English that investigated the Peguero-Lo Presti criteria for detecting LVH were included. Data were independently extracted and analyzed by two investigators.

Results

A total of 51 records were screened, and 6 trials comprising 13,564 patients were finally included. A bivariate analysis showed that the sensitivity of the Peguero-Lo Presti criteria (0.52, 95% confidence interval (CI) 0.46–0.58) was higher than that of the Cornell voltage index (0.29, 95% CI 0.23–0.36) and Sokolow-Lyon criteria (0.24, 95% CI 0.21–0.27); the diagnostic accuracy of the Peguero-Lo Presti criteria (0.69, 95% CI 0.65–0.73) was also higher than that of the Cornell voltage index (0.67, 95% CI 0.62–0.71) and Sokolow-Lyon criteria (0.28, 95% CI 0.25–0.32); and the specificity of the Peguero-Lo Presti criteria (0.85, 95% CI 0.79–0.90) was similar to that of the Cornell voltage index (0.92, 95% CI 0.89–0.95) and Sokolow-Lyon criteria (0.94, 95%CI 0.88–0.97). Two trials (including 12,748 patients) were discharged because they included partly healthy subjects and accounted for substantial heterogeneity. Pooled analysis of the remaining 4 trials (including 816 patients) showed that the sensitivity of the Peguero-Lo Presti criteria (0.56, 95% CI 0.51–0.61) was also higher than that of the Cornell voltage index (0.36, 95% CI 0.31–0.42) and Sokolow-Lyon criteria (0.24, 95% CI 0.18–0.31); the diagnostic accuracy of the Peguero-Lo Presti criteria (0.84, 95% CI 0.80–0.87) was also higher than that of the Cornell voltage index (0.54, 95% CI 0.50–0.58) and Sokolow-Lyon criteria (0.38, 95% CI 0.34–0.42); and the specificity of the Peguero-Lo Presti criteria (0.90, 95% CI 0.87–0.92) was similar to that of the Cornell voltage index (0.93, 95% CI 0.88–0.96) and Sokolow-Lyon criteria (0.97, 95% CI 0.90–0.99). Both the likelihood ratio and posttest probability of the Peguero-Lo Presti criteria and Cornell voltage index were moderate.

Conclusion

Based on this systematic review and meta-analysis, the Peguero-Lo Presti criteria-based ECG diagnostic method for LVH has high sensitivity, specificity and diagnostic accuracy and should be applied in clinical practice settings.

The electrocardiogram: Still a useful marker for LV fibrosis in aortic stenosis

Left ventricular (LV) strain on the electrocardiogram (ECG) (down-sloping, convex ST-segment depression with asymmetric T-wave inversion in leads V5 and V6) reflects fibrosis as a result of subendocardial ischemia. It is associated with a significantly increased risk of cardiovascular events independent of the presence of LV hypertrophy on the echocardiogram or cardiac magnetic resonance (CMR) scan. Ongoing studies of early aortic valve replacement in asymptomatic patients with severe aortic stenosis are using ECG changes as a marker of possible fibrosis shown by midwall late gadolinium enhancement on CMR. However, until these studies report, it is still reasonable to respond to LV strain on the ECG by tightening control of systemic hypertension and consider intervention in cases where indications are otherwise in borderline.

Assessment of electrocardiographic criteria for the diagnosis of left ventricular hypertrophy in the octogenarian population

BACKGROUND

Although there are several electrocardiographic (ECG) diagnostic criteria for identifying left ventricular hypertrophy (LVH), the sensitivity of these criteria remains low. Recently, the Peguero-Lo Presti criterion provides a higher sensitivity than the current criteria. We aimed to test this ECG criterion prospectively, in the octogenarian population.

METHODS

We prospectively enrolled outpatients over 80 years of age who were referred to our echocardiography laboratory. The Peguero-Lo Presti criterion was assessed along with other established ECG criteria. Left ventricular mass was calculated by echocardiography. Performance of ECG indices in diagnosing LVH were evaluated.

RESULTS

Overall, 119 patients were included in the study. The sensitivity and specificity of the Peguero-Lo Presti criterion were 62.5% and 87.3%, respectively. In addition, the highest sensitivity belonged to the Peguero-Lo Presti criterion, and the highest AUC value was also seen in this criterion (AUC: 0.787, 95% CI, 0.698-0.876, P < .001).

CONCLUSION

The Peguero-Lo Presti criteria showed the highest sensitivity for LVH detection, and it outperformed the other validated criteria in this octogenarian population. The Peguero-Lo Presti criteria seemed to be more effective for diagnosing LVH in this setting.

Diagnosis of left ventricular hypertrophy using convolutional neural network

BACKGROUND

Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results.

METHODS

In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle.

RESULTS

The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice.

CONCLUSIONS

Therefore, the measurement method proposed in this paper has the advantages of less manual intervention, and the processing method is reasonable and has practical value.

Current diagnostic ECG criteria for left ventricular hypertrophy: is it time to change paradigm in the analysis of data?

BACKGROUND

Twelve-lead ECG represents the most common diagnostic tool in clinical cardiology and allows an immediate screening of left ventricular hypertrophy (LVH), but current criteria might have poor clinical usefulness in everyday clinical practice due to lack of sensitivity.

METHODS

The current study aims to review and compare the clinical performance of known ECG criteria of LVH in a real-life setting; 2134 patients had ECG and echocardiographic exams performed during the same hospitalization. All traces were retrospectively analysed, and the amplitudes of the waves were manually measured. Transthoracic echocardiography was considered as the gold standard to assess LVH.

RESULTS

LVH had a prevalence of 58%. Considering the diagnostic performance of ECG criteria for LVH, the Cornell voltage carried the best area under the receiver operating characteristic curve (0.678), while RaVF (R wave in aVF lead) had the poorer result (0.440). The R5/R6 criterion had the best sensitivity (60%), but with the worst specificity (37.4%). The 'Q or S aVR' had the best specificity (99.9%) but lacks sensitivity (0.80%). The Peguero Lo Presti criterion had a sensitivity of 42.3% and a specificity of 75.8%. The Cornell voltage and the Cornell product had similar area under the receiver operating characteristic curve values which were found to be significantly greater compared with other criteria.

CONCLUSION

Current ECG criteria of LVH have low sensitivity despite an acceptable specificity. Among these, Cornell voltage and Cornell product criteria were equally found to have a more accurate diagnostic performance compared with other criteria. To overcome the intrinsic limitations of the current ECG LVH criteria, a new paradigm in the analysis of electrocardiographic data might be necessary.

Optimized electrocardiographic criteria for the detection of left ventricular hypertrophy in obesity patients

Background

Despite a generally high specificity, electrocardiographic (ECG) criteria for the detection of left ventricular hypertrophy (LVH) lack sensitivity, particularly in obesity patients.

Objectives

The aim of the study was to evaluate the accuracy of the most commonly used ECG criteria (Cornell voltage and Sokolow‐Lyon index), the recently introduced Peguero‐Lo Presti criteria and the correction of these criteria by body mass index (BMI) to detect LVH in obesity patients and to propose adjusted ECG criteria with optimal accuracy.

Methods

The accuracy of the ECG criteria for the detection of LVH was retrospectively tested in a cohort of obesity patients referred for a transthoracic echocardiogram based on clinical grounds (test cohort, n = 167). Adjusted ECG criteria with optimal sensitivity for the detection of LVH were developed. Subsequently, the value of these criteria was prospectively tested in an obese population without known cardiovascular disease (validation cohort, n = 100).

Results

Established ECG criteria had a poor sensitivity in obesity patients in both the test cohort and the validation cohort. The adjusted criteria showed improved sensitivity, with optimal values for males using the Cornell voltage corrected for BMI, (RaVL+SV3)*BMI ≥700 mm*kg/m²; sensitivity 47% test cohort, 40% validation cohort; for females, the Sokolow‐Lyon index corrected for BMI, (SV1 + RV5/RV6)*BMI ≥885 mm*kg/m²; sensitivity 26% test cohort, 23% validation cohort.

Conclusions

Established ECG criteria for the detection of LVH lack sufficient sensitivity in obesity patients. We propose new criteria for the detection of LVH in obesity patients with improved sensitivity, approaching known sensitivity of the most commonly used ECG criteria in lean subjects.

Influence of relative wall thickness on electrocardiographic voltage measures in left ventricular hypertrophy: a novel factor contributing to poor diagnostic accuracy

Objective: To characterize the influence of relative wall thickness (RWT) on Cornell, Sokolow-Lyon and Peguero-Lo Presti voltages and elucidate its potential impacts on their diagnostic accuracy for LVH in a large general Chinese population.Methods: A total of 10,614 permanent residents aged ≥ 35 years were recruited for this study. All the participants were subjected to ECG and echocardiogram during the same visit. Multivariate linear and logistic regression analyzes were conducted to assess the influence of RWT on the voltages and their diagnostic performance for LVH detection.Results: A distinct correlation was identified between RWT and Cornell and Peguero-Lo Presti voltages following adjustments for age, gender and left ventricular mass (LVM) (β = 0.675 and 1.342, respectively; Ps < 0.001). Besides, subjects with RWT > 0.42 exhibited higher rates of LVH diagnosed by Cornell (OR = 1.78, 95% CI: 1.45-2.20), Sokolow-Lyon (OR = 1.30, 95% CI: 1.08-1.56), and Peguero-Lo Presti voltage (OR = 1.48, 95% CI: 1.29-1.70) after adjustments for age, gender and echocardiographic LVH. Furthermore, concentric remodeling or concentric hypertrophy displayed higher rates of LVH diagnoses via Cornell and Peguero-Lo Presti voltage criteria, as compared with normal geometry or eccentric hypertrophy, respectively (all Ps < 0.05), findings of which were independent of age, gender and LVMI.Conclusion: Echocardiographic RWT was independently correlated with electrocardiographic voltage measures of LVH, which influenced their positive rates and contributed to poor diagnostic performance.

Assessment of a new electrocardiographic criterion for the diagnosis of left ventricle hypertrophy: A prospective validation study

OBJECTIVE:

Many criteria have been developed to predict left ventricular hypertrophy using an electrocardiogram (ECG). However, one major common limitation of all has been their low sensitivity. Based on that, recently, a novel criterion has been proposed, which is believed to have higher sensitivity without a compromise in specificity. Therefore, in our study, we aimed to test this novel ECG criterion prospectively in large, unselected cardiac patients.

METHODS:

Patients who were referred to our echocardiography laboratory due to various etiologies were prospectively enrolled. The novel Peguero-Lo Presti criterion was assessed along with other established ECG criteria. The left ventricular mass index was calculated using echocardiography. The performance of each index was evaluated.

RESULTS:

Overall, 767 patients were enrolled in this study. The sensitivity and specificity of the Peguero-Lo Presti criterion were 17.5% and 94.5%, respectively. Although the highest sensitivity belonged to the Peguero-Lo Presti criterion, in ROC analysis, it showed modest predictive capability, which was similar to the established Cornell voltage criterion (AUC=0.64 [0.56–0.68 95% CI], p<0.01).

CONCLUSION:

Although this novel criterion had higher sensitivity, the overall performance was similar to the current indices. Further adjustments, particularly based on age and body mass index, may yield better results.

The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction

OBJECTIVE

Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, significant deterioration in NI-FECG signal quality occurs during the presence of a highly non-conductive layer known as vernix caseosa which forms on the fetal body surface beginning in approximately the 28th week of gestation. This work investigates asymmetric modeling of vernix caseosa and other maternal-fetal tissues in accordance with clinical observations and assesses their impacts for NI-FECG signal processing.

APPROACH

We develop a process for simulating dynamic maternal-fetal abdominal ECG mixtures using a synthetic cardiac source model embedded in a finite element volume conductor. Using this process, changes in NI-FECG signal morphology are assessed in an extensive set of finite element models including spatially variable distributions of vernix caseosa.

MAIN RESULTS

Our simulations show that volume conductor asymmetry can result in over 70% error in the observed T/QRS ratio and significant changes to signal morphology compared to a homogeneous volume conductor model. Volume conductor effects must be considered when analyzing T/QRS ratios obtained via NI-FECG and should be considered in future algorithm benchmarks using simulated data.

SIGNIFICANCE

This work shows that without knowledge of the influence of volume conductor effects, clinical evaluation of the T/QRS ratio derived via NI-FECG should be avoided.

Newly proposed electrocardiographic criteria for the diagnosis of left ventricular hypertrophy in a Chinese population

BACKGROUND

The electrocardiographic criteria currently available for the diagnosis of left ventricular hypertrophy (LVH) are low in sensitivity. Thus, we compared the diagnostic performance of newly proposed electrocardiographic criteria to the existing criteria in a Chinese population.

METHODS

A total of 235 consecutive hypertensive patients, hospitalized in our department between May 2017 and April 2018, were included. They were divided into two groups based on the gold standard echocardiogram: those with (n = 116) and without LVH (n = 119). The newly proposed ECG criteria were calculated by summating the amplitude of the deepest S wave (S_D ) in any single lead and the S-wave amplitude of lead V₄ (S_V4 ). The area under the curve was calculated and compared against the sex-specific Cornell limb lead and Sokolow-Lyon criteria.

RESULTS

ECG analysis of the cohort showed that the newly proposed criteria had the highest sensitivity in diagnosing LVH (male: 65.5%; female: 81%), followed by the Cornell limb lead criteria (male: 55.2%; female: 56.9%). The specificities of both sets of criteria were higher than 70%, with no significant differences between them. Receiver operator curve analysis showed an optimal cutoff of ≥2.1 mV for females (AUC: 0.832; 95% CI: 0.757-0.906) and ≥2.6 mV for males (AUC: 0.772; 95% CI: 0.687-0.856).

CONCLUSION

The newly proposed S_D  + S_V4 criteria provide an improved sensitivity for the ECG diagnosis of LVH compared to existing criteria, but its routine use will require further validation in larger populations.

Limited Relationship of Voltage Criteria for Electrocardiogram Left Ventricular Hypertrophy to Cardiovascular Mortality

BACKGROUND

Numerous methods have been proposed for diagnosing left ventricular hypertrophy using the electrocardiogram. They have limited sensitivity for recognizing pathological hypertrophy, at least in part due to their inability to distinguish pathological from physiological hypertrophy. Our objective is to compare the major electrocardiogram-left ventricular hypertrophy criteria using cardiovascular mortality as a surrogate for pathological hypertrophy.

METHODS

This study was a retrospective analysis of 16,253 veterans < 56 years of age seen at a large Veterans Affairs Medical Center from 1987 to 1999 and followed a median of 17.8 years for cardiovascular mortality. Receiver operating characteristics and Cox hazard survival techniques were applied.

RESULTS

Of the 16,253 veterans included in our target population, the mean age was 43 years, 8.6% were female, 33.5% met criteria for electrocardiogram-left ventricular hypertrophy, and there were 744 cardiovascular deaths (annual cardiovascular mortality 0.25%). Receiver operating characteristic analysis demonstrated that the greatest area under the curve (AUC) for classification of cardiovascular death was obtained using the Romhilt-Estes score (0.63; 95% confidence interval, 0.61-0.65). Most of the voltage-only criteria had nondiagnostic area under the curves, with the Cornell being the best at 0.59 (95% confidence interval, 0.57-0.62). When the components of the Romhilt-Estes score were examined using step-wise Wald analysis, the voltage criteria dropped from the model. The Romhilt-Estes score ≥ 4, the Cornell, and the Peguero had the highest association with cardiovascular mortality (adjusted hazard ratios 2.2, 2.0, and 2.1, consecutively).

CONCLUSION

None of the electrocardiogram leads with voltage criteria exhibited sufficient classification power for clinical use.

Electrocardiographic Criteria for the Diagnosis of Left Ventricular Hypertrophy

BACKGROUND

Current electrocardiographic (ECG) criteria for the diagnosis of left ventricular hypertrophy (LVH) have low sensitivity.

OBJECTIVES

The goal of this study was to test a new method to improve the diagnostic performance of the electrocardiogram.

METHODS

The study was divided into 2 groups, a test and a validation cohort. In the test cohort, 94 patients were analyzed, including 47 with the diagnosis of hypertensive crisis and 47 with normal blood pressure at admission. Echocardiography was used to estimate the left ventricular mass index. Area under the curve (AUC) analysis was used for comparison of single and combined leads. The McNemar test was used to assess agreement among the ECG criteria against the left ventricular mass index. The proposed ECG criteria involved measuring the amplitude of the deepest S wave (S_D) in any single lead and adding it to the S wave amplitude of lead V₄ (SV₄). Currently accepted LVH ECG criteria such as Cornell voltage and Sokolow-Lyon were used for comparison. The validation cohort consisted of 122 consecutive patients referred for an echocardiogram regardless of the admitting diagnosis.

RESULTS

The S_D was the most accurate single lead measurement for the diagnosis of LVH (AUC: 0.80; p < 0.001). When both cohorts were analyzed, the S_D + SV₄ criteria outperformed Cornell voltage with a significantly higher sensitivity (62% [95% confidence interval [CI]: 50% to 72%] vs. 35% [95% CI: 24% to 46%]). The specificities of all the criteria were ≥90%, with no significant difference among them.

CONCLUSIONS

The proposed criteria for the ECG diagnosis of LVH improved the sensitivity and overall accuracy of the test.

Sex-Specific Response to Cardiac Resynchronization Therapy: Effect of Left Ventricular Size and QRS Duration in Left Bundle Branch Block

OBJECTIVES

In this study, the authors sought to assess the impact of body and heart size on sex-specific cardiac resynchronization therapy (CRT) response rate, according to QRS duration (QRSd) as a continuum.

BACKGROUND

Effects of CRT differ between sexes for any given QRSd.

METHODS

New York Heart Association functional class III/IV patients with nonischemic cardiomyopathy and "true" left bundle branch block (LBBB) were evaluated. Left ventricular mass (LVM) and end-diastolic volume were measured echocardiographically. Positive response was defined by left ventricular ejection fraction (LVEF) improvement post-CRT.

RESULTS

Among 130 patients (LVEF 19 ± 7.1%; QRSd 165 ± 20 ms; 55% female), CRT improved LVEF to 32 ± 14% (p < 0.001) during a median 2 years follow-up. Positive responses occurred in 103 of 130 (79%) (78% when QRSd <150 ms vs. 80% when QRSd ≥150 ms; p = 0.8). Body surface area (BSA), QRSd, and LVM were lower in women, but QRSd/LVM ratio greater (p < 0.0001). Sexes did not differ for pharmacotherapy and comorbidities, but female CRT response was greater: 90% (65 of 72) versus 66% (38 of 58) in males (p < 0.001). With QRSd as a continuum, the overall CRT-response relationship showed a progressive increase to plateau between 150 and 170 ms, then a decrease. Sex-specific differences were conspicuous: among females, a peak effect was observed between 135 and 150 ms, thereafter a decline, with the male response rate lower, but with a gradual increase as QRSd lengthened. Sex-specific differences were unaltered by BSA, but resolved with integration of LVM or end-diastolic volume.

CONCLUSIONS

Sex differences in the QRSd-response relationship among CRT patients with LBBB were unexplained by application of strict LBBB criteria or by BSA, but resolved by QRSd normalization for heart size using LV mass or volume.

Diffuse Myocardial Fibrosis Reduces Electrocardiographic Voltage Measures of Left Ventricular Hypertrophy Independent of Left Ventricular Mass

Background

Myocardial fibrosis quantified by myocardial extracellular volume fraction (ECV) and left ventricular mass (LVM) index (LVMI) measured by cardiovascular magnetic resonance might represent independent and opposing contributors to ECG voltage measures of left ventricular hypertrophy (LVH). Diffuse myocardial fibrosis can occur in LVH and interfere with ECG voltage measures. This phenomenon could explain the decreased sensitivity of LVH detectable by ECG, a fundamental diagnostic tool in cardiology.

Methods and Results

We identified 77 patients (median age, 53 [interquartile range, 26–60] years; 49% female) referred for contrast‐enhanced cardiovascular magnetic resonance with ECV measures and 12‐lead ECG. Exclusion criteria included clinical confounders that might influence ECG measures of LVH. We evaluated ECG voltage‐based LVH measures, including Sokolow‐Lyon index, Cornell voltage, 12‐lead voltage, and the vectorcardiogram spatial QRS voltage, with respect to LVMI and ECV. ECV and LVMI were not correlated (R²=0.02; P=0.25). For all voltage‐related parameters, higher LVMI resulted in greater voltage (r=0.33–0.49; P<0.05 for all), whereas increased ECV resulted in lower voltage (r=−0.32 to −0.57; P<0.05 for all). When accounting for body fat, LV end‐diastolic volume, and mass‐to‐volume ratio, both LVMI (β=0.58, P=0.03) and ECV (β=−0.46, P<0.001) were independent predictors of QRS voltage (multivariate adjusted R²=0.39; P<0.001).

Conclusions

Myocardial mass and diffuse myocardial fibrosis have independent and opposing effects upon ECG voltage measures of LVH. Diffuse myocardial fibrosis quantified by ECV can obscure the ECG manifestations of increased LVM. This provides mechanistic insight, which can explain the limited sensitivity of the ECG for detecting increased LVM.

The 4th Report of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy

The 4th Report provides a brief review of publications focused on the electrocardiographic diagnosis of left ventricular hypertrophy published during the period of 2010 to 2016 by the members of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy. The Working Group recommended that ECG research and clinical attention be redirected from the estimation of LVM to the identification of electrical remodeling, to better understanding the sequence of events connecting electrical remodeling to outcomes. The need for a re-definition of terms and for a new paradigm is also stressed.

ECG low QRS voltage and wide QRS complex predictive of centenarian 360-day mortality

We examined the electrocardiographic (ECG) findings of centenarians and associated them with >360-day survival. Physical and functional assessment, resting electrocardiogram and laboratory tests were performed on 86 study participants 101.9 ± 1.2 years old (mean ± SD) (70 women, 16 men) and followed for at least 360 days. Centenarian ECGs were assessed for left ventricular hypertrophy (LVH) according to the Romhilt-Estes score, Sokolow-Lyon criteria and Cornell voltage criteria which were positive for 12.8, 6.98, and 10.5 % of participants, respectively. Fifty-two study participants (60 %) survived ≥360 days. Multivariate logistic regression analysis revealed a negative relationship between 360-day survival and the following: R II <0.45 mV adjusted for CRP (odds ratio (OR) = 0.108, 95 % confidence interval (CI) = 0.034-0.341, P < .001), R aVF < 0.35 mV adjusted for CRP (OR = 0.151, 95 % CI = 0.039-0.584, P < .006), Sokolow-Lyon voltage <1.45 mV adjusted for CRP (OR = 0.178, 95 % CI = 0.064-0.492, P = .001), QRS ≥90 ms adjusted for CRP (OR = 0.375, 95 % CI = 0.144-0.975, P = .044), and Romhilt-Estes score ≥5 points adjusted for sex and Barthel Index (OR = 0.459, 95 % CI = 0.212-0.993, P = .048) in single variable ECG models. QRS voltage correlated positively with systolic and pulse pressure, serum vitamin B12 level, sodium, calcium, phosphorous, TIMP-1, and eGFR. QRS voltage correlated negatively with BMI, WHR, serum leptin, IL-6, TNF-α, and PAI-1 levels. QRS complex duration correlated positively with CRP; QTc correlated positively with TNF-α. Results suggest that Romhilt-Estes LVH criteria scores ≥5 points, low ECG QRS voltages (Sokolow-Lyon voltage <1.45 mV), and QRS complexes ≥90 ms are predictive of centenarian 360-day mortality.