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

Revolutionizing LVH detection using artificial intelligence: the AI heartbeat project

Many studies have shown the utility and promise of artificial intelligence (AI), for the diagnosis of left ventricular hypertrophy (LVH). The aim of the present study was to conduct a meta-analysis to compare the accuracy of AI tools to electrocardiographic criteria, including Sokolow-Lyon and the Cornell, most commonly used for the detection of LVH in clinical practice. Nine studies meeting the inclusion criteria were selected, comprising a sample size of 31 657 patients in the testing and 100 271 in the training datasets. Meta-analysis was performed using a hierarchal model, calculating the pooled sensitivity, specificity, accuracy, along with the 95% confidence intervals (95% CIs). To ensure that the results were not skewed by one particular study, a sensitivity analysis using the 'leave-out-one approach' was adopted for all three outcomes. AI was associated with greater pooled estimates; accuracy, 80.50 (95% CI: 80.4-80.60), sensitivity, 89.29 (95% CI: 89.25-89.33) and specificity, 93.32 (95% CI: 93.26-93.38). Adjusting for weightage of individual studies on the outcomes, the results showed that while accuracy and specificity were unchanged, the adjusted pooled sensitivity was 53.16 (95% CI: 52.92-53.40). AI demonstrates higher diagnostic accuracy and sensitivity compared with conventional ECG criteria for LVH detection. AI holds promise as a reliable and efficient tool for the accurate detection of LVH in diverse populations. Further studies are needed to test AI models in hypertensive populations, particularly in low resource settings.

Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

Background

Traditional ECG criteria for left ventricular hypertrophy (LVH) have low diagnostic yield. Machine learning (ML) can improve ECG classification.

Methods

ECG summary features (rate, intervals, axis), R-wave, S-wave and overall-QRS amplitudes, and QRS/QRST voltage-time integrals (VTIs) were extracted from 12-lead, vectorcardiographic X-Y-Z-lead, and root-mean-square (3D) representative-beat ECGs. Latent features were extracted by variational autoencoder from X-Y-Z and 3D representative-beat ECGs. Logistic regression, random forest, light gradient boosted machine (LGBM), residual network (ResNet) and multilayer perceptron network (MLP) models using ECG features and sex, and a convolutional neural network (CNN) using ECG signals, were trained to predict LVH (left ventricular mass indexed in women >95 g/m², men >115 g/m²) on 225,333 adult ECG-echocardiogram (within 45 days) pairs. AUROCs for LVH classification were obtained in a separate test set for individual ECG variables, traditional criteria and ML models.

Results

In the test set (n=25,263), AUROC for LVH classification was higher for ML models using ECG features (LGBM 0.790, MLP 0.789, ResNet 0.788) as compared to the best individual variable (VTIQRS-3D 0.677), the best traditional criterion (Cornell voltage-duration product 0.647) and CNN using ECG signal (0.767). Among patients without LVH who had a follow-up echocardiogram >1 (closest to 5) years later, LGBM false positives, compared to true negatives, had a 2.63 (95% CI 2.01, 3.45)-fold higher risk for developing LVH (p<0.0001).

Conclusions

ML models are superior to traditional ECG criteria to classify-and predict future-LVH. Models trained on extracted ECG features, including variational autoencoder latent variables, outperformed CNN directly trained on ECG signal.

Correlation between malnutrition and mortality in older patients aged ≥90 years with multimorbidity

OBJECTIVES

Malnutrition is prevalent in geriatric patients and associated with poor prognosis. This study aimed to evaluate the incremental prognostic value of different nutritional assessment tools in patients (90 years and older) with multimorbidity in China.

METHODS

Patients aged ≥90 years with multimorbidity from the Geriatric Research Center in Nanjing Jinling Hospital from January 1, 2008 to December 31, 2018 were analyzed. Patients were followed until December 31,2022. The nutrition status was assessed according to the mini nutritional assessment (MNA), the geriatric nutritional risk index (GNRI), and the prognostic nutritional index score (PNI), respectively. The outcome was all-cause death. Kaplan-Meier method was used to draw the survival curve, and univariate and multivariate Cox proportional risk regression analysis was used to explore the influencing factors of all-cause death in the patients.

RESULTS

160 participants aged 90(90,91) years were included. During a median follow-up of 5.41(3.12-7.64) years, 106(66.25 %) patients died. Deceased patients had lower MNA [20.75(16.75,23.00) vs. 26.00(24.00,26.00); p < 0.001], lower GNRI [96.21±8.75 vs. 100.94±6.80; p = 0.001] and lower PNI [46.16(40.77,49.57) vs. 47.75(45.36,51.53); p = 0.010] than did survivors. According to MNA, GNRI, and PNI score, 93(58.1 %), 72(45.0 %) and 41(25.6 %) of participants were at risk of malnutrition. Multivariate analysis revealed that malnutrition was independently associated with increased risk for mortality by MNA score (adjusted hazard ratio [HR] 2.502, 95 % confidence interval [CI] 1.561-4.009, p<0.001), GNRI score (adjusted HR 1.650, 95 % CI, 1.117-2.438, p = 0.012), and PNI score (adjusted HR 2.894, 95 % CI, 1.891-4.431, p<0.001). Furthermore, the inclusion of malnutrition indicators in the survival prediction model significantly improved the predictive power of mortality.

CONCLUSION

The risk of malnutrition, as assessed by MNA, GNRI and PNI, in long-lived patients with multimorbidity is a strong independent predictor of mortality and adds significant prognostic information to the survival models.

Electrocardiographic Abnormalities in Nigerian Hypertensives With Echocardiographic Left Ventricular Hypertrophy

Introduction To enhance the diagnosis of anatomic left ventricular hypertrophy (LVH) using electrocardiography (ECG), we aimed to identify common ECG amplitude and non-amplitude abnormalities in Nigerian patients with hypertensive echocardiographic LVH. Method The study included 1,765 patients with essential hypertension aged 18 years and older from the Federal Medical Centre Abuja Hypertension Registry (FMCAHR). Participants underwent echocardiography and ECG following the American College of Cardiology and the American Society of Echocardiography guidelines. Results The prevalence of overall ECG LVH amplitude criteria (43.8%) and individual criteria of Cornell voltage (27.1%), Sokolow-Lyon voltage (23.2%), and Gubner-Ungerleider (13.9%) were higher than non-amplitude ECG abnormalities among patients with echocardiographic LVH. The sensitivity and specificity of LVH criteria were 43.8% and 79.5% for overall ECG LVH, 23.2% and 87.2% for Sokolow-Lyon voltage, 27.1% and 93.3% for Cornell voltage, and 13.9% and 95.4% for Gubner-Ungerleider criteria, respectively. After multivariable adjustment, non-amplitude ECG changes, including prolonged corrected QT (QTc) (odds ratio (OR): 1.68, 95% confidence interval (CI): 1.06-2.66), left ventricular (LV) strain pattern (OR: 1.83, CI: 1.23-2.72), left axis deviation (OR: 1.56, CI: 1.09-2.24), poor R wave progression (OR: 2.36, CI: 1.40-3.97), premature ventricular contractions (OR: 1.80, CI: 1.10-2.91), premature atrial contractions (OR: 2.06, CI: 1.10-3.87), atrial fibrillation (OR: 2.40, CI: 1.20-4.82), and left atrial abnormality (OR: 8.43, CI: 2.95-24.05), were associated with echocardiographic LVH (p < 0.05). Conclusion In our cohort of hypertensive patients, ECG LVH amplitude criteria were the most frequently observed abnormalities associated with echocardiographic LVH. Our findings suggest that despite the low sensitivity, ECG LVH amplitude criteria may remain valuable in diagnosing echocardiographic LVH.

Factors associated with electrocardiographic left ventricular hypertrophy among patients with hypertension in Thailand

BACKGROUND

Left ventricular hypertrophy (LVH) strongly predicts cardiovascular diseases (CVD) and death. One-fourth of Thai adults suffer from hypertension. Nevertheless, the information on LVH among Thai patients with hypertension is not well characterized. We aimed to identify the prevalence and factors associated with electrocardiographic LVH (ECG-LVH) among patients with hypertension in Thailand.

METHODS

The present study obtained the dataset from the Thailand Diabetes Mellitus/Hypertension study, which included hypertension patients aged 20 years and older receiving continuous care at outpatient clinics in hospitals nationwide in 2011-2015 and 2018. Meanwhile, those without a record of 12-lead electrocardiography (ECG) were excluded from the analysis. ECG-LVH was defined as the LVH noted regarding ECG interpretation in the medical records. Multivariable logistic regression analysis was utilized for determining factors associated with ECG-LVH and presented as the adjusted odds ratio (AOR) and 95% confidence interval (CI).

RESULTS

From 226,420 hypertensive patients in the Thailand Diabetes Mellitus/Hypertension study, 38,807 individuals (17.1%) with ECG data recorded were included in the analysis. The mean age was 64.8 ± 11.5 years, and 62.2% were women. Overall, 1,557 study participants had ECG-LVH, with an estimated prevalence of 4.0% (95% CI, 3.8-4.2%). Age-adjusted ECG-LVH prevalence among women and men was 3.4 and 5.1%, respectively (P < 0.001). Multivariable analysis determined factors associated with ECG-LVH, including being men (AOR, 1.49; 95% CI, 1.31-1.69), individuals aged 70 to 79 years (AOR, 1.56; 95% CI, 1.20-2.02) and ≥ 80 years (AOR, 2.10; 95% CI, 1.58-2.78) compared to individuals aged less than 50 years, current smokers (AOR, 1.26; 95% CI, 1.09-1.46) compared to those who never smoked, systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg (AOR, 1.58; 95% CI, 1.30-1.92) compared to systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg.

CONCLUSIONS

The current study illustrated the prevalence of ECG-LVH among Thai patients with hypertension who had ECG recorded and identified high-risk groups who tended to have ECG-LVH. The findings underscore the need for targeted interventions, particularly among high-risk groups such as older individuals, men, and current smokers, to address modifiable factors associated with ECG-LVH.

Do We Need New Electrocardiographic Criteria for Left Ventricular Hypertrophy? The Case of the Peguero-Lo Presti Criterion. A Narrative Review

The cardiovascular risk associated with left ventricular hypertrophy (LVH) in the community and, particularly, in the hypertensive fraction of the general population, represents the rationale for its timely and accurate identification in order to implement adequate preventive strategies. Although electrocardiography (ECG) is the first-line and most economical method of diagnosing LVH its accuracy is largely suboptimal. Over the last 70 years, dozens of different ECG criteria, mostly based on measurements of QRS voltages, have been proposed. In this long journey, a few years ago Peguero et al. developed a novel ECG voltage criterion, currently recognized as Peguero-Lo Presti (PLP) suggesting that it has greater sensitivity than traditional ECG-LVH criteria. Considering that in the last 5 years numerous studies have investigated the diagnostic value of this new index, this review aimed to summarize the data published so far on this topic focusing both on the accuracy in identifying the presence of LVH compared with imaging techniques such as echocardiography (ECHO) and magnetic resonance imaging (MRI) and the value in predicting hard outcomes. The evidence in favor of the greater diagnostic accuracy of the PLP criterion in detecting LVH, phenotyped by ECHO or MRI, and in the stratification of hard outcomes compared with traditional ECG criteria does not appear to be sufficiently proven. Given that the diagnosis of LVH by all ECG criteria (including the PLP) exclusively based on the QRS amplitude is largely imprecise, the development of new multiparametric ECG criteria based on artificial intelligence could represent a real improvement in the diagnostic capacity of the ECG.

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.

Evaluation of Different Criteria in the Diagnosis of Left Ventricular Hypertrophy by Electrocardiogram in Comparison With Echocardiogram

Left ventricular hypertrophy (LVH) is an adaptive mechanism of the cardiac muscle due to increased activity demand or functional overload. The echocardiogram (ECHO) presents a better performance in relation to the electrocardiogram (ECG) for the diagnosis of LVH. However, the ECG is a low-cost and easy-to-reproduce diagnostic alternative and can be useful in services and locations where the ECHO is not yet easily accessible. Recently, a new criterion for LVH was proposed by Peguero-Lo Presti. The aim of this article was to evaluate the electrocardiographic criteria for the diagnosis of LVH (Sokolow-Lyon, Cornell voltage, Romhilt-Estes, and Peguero-Lo Presti) in comparison to the diagnosis made by the ECHO. ECHO and ECG from 142 patients' medical records were analyzed. Patients were divided into three groups according to the ECHO - control, eccentric LVH, and concentric LVH. Sensitivity, specificity, PPV, NPV, and accuracy of the four electrocardiographic criteria were evaluated in three scenarios - (1) LVH vs control, (2) concentric LVH vs control, and (3) eccentric LVH vs control. Of the 142 patients included in the study, 100 (70.4%) had LVH. According to the type of hypertrophy, the 100 patients with LVH were divided into two groups - 41 (28.8%) had eccentric LVH and 59 (41.5%) had concentric LVH. Of all the scenarios, the Peguero-Lo Presti criteria obtained the best sensitivity (1, 41%; 2, 33,9%; 3, 51,2%) and accuracy (1, 56,3%; 2, 58,4%; 3, 69,8%). The Sokolow-Lyon criteria showed greater specificity in all analyses (100%). None of the electrocardiographic criteria obtained sensitivity values ​​that would justify the use of the electrocardiogram as a screening test for LVH. No differences were found for eccentric and concentric LVH in terms of diagnosis using electrocardiographic criteria. We recommend the use of these criteria to confirm the diagnosis of LVH, especially in low-complexity services that do not have image-based diagnostic tests.

Diagnostic Ability of Peguero-Lo Presti Electrocardiographic Left Ventricular Hypertrophy Criterion in Severe Aortic Stenosis

Traditional electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH), introduced in the pre-echocardiographic era of diagnosis, have a relatively low sensitivity (usually not exceeding 25–40%) in detecting LVH. A novel Peguero-Lo Presti ECG-LVH criterion was recently shown to exhibit a higher sensitivity than the traditional ECG-LVH criteria in hypertension. Our aim was to test the diagnostic ability of the novel Peguero-Lo Presti ECG-LVH criterion in severe aortic stenosis. We retrospectively analyzed 12-lead ECG tracings and echocardiographic records from the index hospitalization of 50 patients with isolated severe aortic stenosis (mean age: 77 ± 10 years; 30 women and 20 men). Exclusion criteria included QRS > 120 ms, bundle branch blocks or left anterior fascicular block, a history of myocardial infarction, more than mild aortic or mitral regurgitation, and significant LV dysfunction by echocardiography. We compared the agreement of the novel Peguero-Lo Presti criterion and traditional ECG-LVH criteria with echocardiographic LVH (LV mass index > 95 g/m² in women and >115 g/m² in men). Echocardiographic LVH was found in 32 out of 50 study patients. The sensitivity of the Peguero-Lo Presti criterion in detecting LVH was improved (55% vs. 9–34%) at lower specificity (72% vs. 78–100%) in comparison to 8 single traditional ECG-LVH criteria. Additionally, the positive predictive value (77% vs. 72%), positive likelihood ratio (2.0 vs. 1.5), and odds ratio (3.2 vs. 2.4) were higher for the Peguero-Lo Presti criterion versus the presence of any of these 8 traditional ECG-LVH criteria. Cohen’s Kappa, a measure of concordance between ECG and echocardiography with regard to LVH, was 0.24 for the Peguero-Lo Presti criterion, −0.01–0.13 for single traditional criteria, and 0.20 for any traditional criterion. However, by the receiver operating characteristics (ROC) curve analysis, the overall ability to discriminate between patients with and without LVH was insignificantly lower for the Peguero-Lo Presti versus Cornell voltage as a continuous variable (area under the ROC curve: 0.65 (95% CI, 0.48–0.81) vs. 0.71 (0.55–0.86), p = 0.5). In conclusion, our preliminary results suggest a slightly better, albeit still low, agreement of the novel Peguero-Lo Presti ECG criterion compared to the traditional ECG-LVH criteria with echocardiographic LVH in severe aortic stenosis.

Clinical applicability and diagnostic performance of electrocardiographic criteria for left ventricular hypertrophy diagnosis in older adults

Recently, a new ECG criterion, the Peguero-Lo Presti (PLP), improved overall accuracy in the diagnosis of left ventricular hypertrophy (LVH)—compared to traditional ECG criteria, but with few patients with advanced age. We analyzed patients with older age and examined which ECG criteria would have better overall performance. A total of 592 patients were included (83.1% with hypertension, mean age of 77.5 years) and the PLP criterion was compared against Cornell voltage (CV), Sokolow-Lyon voltage (SL) and Romhilt-Estes criteria (cutoffs of 4 and 5 points, RE4 and RE5, respectively) using LVH defined by the echocardiogram as the gold standard. The PLP had higher AUC than the CV, RE and SL (respectively, 0.70 vs 0.66 vs 0.64 vs 0.67), increased sensitivity compared with the SL, CV and RE5 (respectively, 51.9% [95% CI 45.4–58.3%] vs 28.2% [95% CI 22.6–34.4%], p < 0.0001; vs 35.3% [95% CI 29.2–41.7%], p < 0.0001; vs 44.4% [95% CI 38.0–50.9%], p = 0.042), highest F1 score (58.3%) and net benefit for most of the 20–60% threshold range in the decision curve analysis. Overall, despite the best diagnostic performance in older patients, the PLP criterion cannot rule out LVH consistently but can potentially be used to guide clinical decision for echocardiogram ordering in low-resource settings.

Diagnostic Accuracy of the Electrocardiography Criteria for Left Ventricular Hypertrophy (Cornell Voltage Criteria, Sokolow-Lyon Index, Romhilt-Estes, and Peguero-Lo Presti Criteria) Compared to Transthoracic Echocardiography

Objective/Aim: We aimed to evaluate the diagnostic utility of the widely used left ventricular hypertrophy (LVH) electrocardiography (ECG) criteria (Cornell Voltage Criteria [CVC], Sokolow-Lyon Index [SLI], Romhilt-Estes [REC], and Peguero-Lo Presti [PLP] Criteria) compared with the left ventricular mass measured by echocardiography.

Methods: In this prospective diagnostic accuracy study, we screened all consecutive adults (18 to 65 years) who presented to our academic emergency department (ED) with increased blood pressure (≥130/85 mmHg) between January 2016 and January 2017, and we enrolled a convenience sample of 165 patients in our study. The attending emergency physician managed all patients as per their primary complaint. The consulting cardiologist performed a transthoracic echocardiogram (TTE) of the patient and calculated the left ventricular mass (LVM) according to the American Society of Echocardiography (ASE) formula. After completing the patient recruitment phase, researchers evaluated all ECGs and calculated scores for SLI, CVC, REC, and PLP. We used contingency tables to calculate the diagnostic utility metrics of all ECG criteria.

Results: The prevalence of LVH by TTE was 31.5%. CVC, SLI, REC, and PLP criteria correctly identified (true positive rate) abnormal LVM in only 3.9%, 1.9%, 9.6%, and 19.2% of the patients, respectively. CVC, SLI, REC score and PLP criteria performed poorly with extremely low sensitivities (3.9%, 1.9%, 10%, 19.2%) and poor accuracies (67.3%, 64.9%, 57.7%, 69.7%).

Conclusion: ECG voltage criteria's clinical utility in estimating LVM and LVH is low, and it should not be used for this purpose.

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.

A meta-analytic evaluation of the diagnostic accuracy of the electrocardiographic Peguero-Lo Presti criterion for left ventricular hypertrophy

Although electrocardiography (ECG) is a cost-effective and convenient tool for routine screening of left ventricular hypertrophy (LVH), its performance has been shown to be poor. The Peguero-Lo Presti, a novel voltage criterion, was found to be potentially better than the most commonly used criteria. We conducted a systematic review and meta-analysis of its diagnostic accuracy compared to Cornell and Sokolow-Lyon voltage criteria. Bibliographic databases were searched to identify relevant articles. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (ROC) curves were performed for comparison. Ten studies reporting data from 5984 individuals were included in the meta-analysis. Peguero-Lo Presti had the highest pooled sensitivity (43.0%, 95% confidence interval [CI]: 30.2-56.9) followed by Cornell (26.1%; 95% CI: 16.9-37.9) and Sokolow Lyon (22.0%; 95% CI: 14.1-32.7). However, Peguero-Lo Presti had the lesser pooled specificity (90.5%; 95% CI: 86.3-93.5) and Cornell the highest (94.9%; 95% CI: 90.3-97.3). The pooled DOR was 6.63 (95% CI: 3.95-11.13), 5.50 (95% CI: 3.64-8.30), and 2.94 (95% CI: 2.20-3.92) for Peguero-Lo Presti, Cornell, and Sokolow-Lyon, respectively. Peguero-Lo Presti had the best accuracy according to summary ROC curves, with an area under the curve of 0.827 compared to 0.715 for Cornell, and 0.623 for Sokolow-Lyon. In conclusion, according to this meta-analysis, Peguero-Lo Presti has a better diagnostic performance than Cornell and Sokolow-Lyon and might be more useful in routine clinical practice as a screening tool for LVH.

ECG Indices Poorly Predict Left Ventricular Hypertrophy and Are Applicable Only in Individuals With Low Cardiovascular Risk

Background: Left ventricular hypertrophy (LVH) is an important risk factor for cardiovascular events. The electrocardiography (ECG) has poor sensitivity, but it is commonly used to detect LVH.

AIM

To evaluate the diagnostic efficacy of known ECG indicators to recognize LVH in subgroups with different cardiovascular risk levels. Methods: 676 volunteers were included.

RESULTS

We found that 10.2% of the analyzed population had LVH based on echocardiography. Individuals with LVH were older, had a higher body mass index, higher systolic blood pressure, lower heart rate, higher parameters of insulin resistance, higher cardiovascular risk, and android-type obesity. Variables that remained independently associated with LVH were QRS duration, left atrial volume index, troponin T, and hemoglobin A1c. The receiver operating characteristics (ROC) curve analysis of the Sokolow-Lyon index did not show a significant predictive ability to diagnose LVH in the whole study population including all cardiovascular risk classes. The ROC curves analysis of Cornell and Lewis indices showed a modest predictive ability to diagnose LVH in the general population and in a low cardiovascular class.

CONCLUSIONS

There is a need for new, simple methods to diagnose LVH in the general population in order to properly evaluate cardiovascular risk and introduce optimal medical treatment of concomitant disease.