A meta-analytic evaluation of the diagnostic accuracy of the electrocardiographic Peguero-Lo Presti criterion for left ventricular hypertrophy.
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ABSTRACT:
SUBMITTER: Noubiap JJ
PROVIDER: S-EPMC8029852 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC7219774 | biostudies-literature
Project description:Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.
| S-EPMC9924839 | biostudies-literature
Project description:Previous studies to determine the sensitivity of the electrocardiogram (ECG) for left ventricular hypertrophy (LVH) in children had their imperfections: they were not done on an unselected hospital population, several criteria used in adults were not applied to children, and obsolete limits of normal for the ECG parameters were used. Furthermore, left ventricular mass (LVM) was taken as the reference standard for LVH, with no regard for other clinical evidence. The study population consisted of 832 children from whom a 12-lead ECG and an M-mode echocardiogram were taken on the same day. The validity of the ECG criteria was judged on the basis of an abnormal LVM index, either alone or in combination with other clinical evidence. The ECG criteria were based on recently established age-dependent normal limits. At 95% specificity, the ECG criteria have low sensitivities (<25%) when an elevated LVM index is taken as the reference for LVH. When clinical evidence is also taken into account, the sensitivity improved considerably (<43%). Sensitivities could be further improved when ECG parameters were combined. The sensitivity of the pediatric ECG in detecting LVH is low but depends strongly on the definition of the reference used for validation.
| S-EPMC2525851 | biostudies-other
Project description: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.
| S-EPMC8169892 | biostudies-literature
Project description:BACKGROUND:Presence of left ventricular hypertrophy on an ECG (ECG-LVH) is widely assessed clinically and provides prognostic information in some settings. There is evidence for significant heritability of ECG-LVH. We conducted a large-scale gene-centric association analysis of 4 commonly measured indices of ECG-LVH. METHODS AND RESULTS:We calculated the Sokolow-Lyon index, Cornell product, 12-lead QRS voltage sum, and 12-lead QRS voltage product in 10 256 individuals from 3 population-based cohorts and typed their DNA using a customized gene array (the Illumina HumanCVD BeadChip 50K array), containing 49 094 genetic variants in ?2100 genes of cardiovascular relevance. We followed-up promising associations in 11 777 additional individuals. We identified and replicated 4 loci associated with ECG-LVH indices: 3p22.2 (SCN5A, rs6797133, P=1.22 × 10(-7)) with Cornell product and 12q13.3 (PTGES3, rs2290893, P=3.74 × 10(-8)), 15q25.2 (NMB, rs2292462, P=3.23 × 10(-9)), and 15q26.3 (IGF1R, rs4966014, P=1.26 × 10(-7)) with the 12-lead QRS voltage sum. The odds ratio of being in the top decile for the 12-lead QRS voltage sum for those carrying 6 trait-raising alleles at the 12q13.3, 15q25.2, and 15q26.3 loci versus those carrying 0 to 1 alleles was 1.60 (95% CI: 1.20 to 2.29). Lead single-nucleotide polymorphisms at the 12q13.3 and 15q25.2 loci showed significant expression quantitative trait loci effects in monocytes. CONCLUSIONS:These findings provide novel insights into the genetic determination of ECG-LVH. The findings could help to improve our understanding of the mechanisms determining this prognostically important trait.
| S-EPMC5291170 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:Carotid intima-media thickness and electrocardiographic left ventricular hypertrophy are 2 subclinical cardiovascular disease measures associated with increased risk of total and ischemic strokes. Increased intima-media thickness and electrocardiographic left ventricular hypertrophy also may reflect end-organ hypertensive effects. Information is scant on the associations of these subclinical measures with intracerebral hemorrhage (ICH). We hypothesized that greater carotid intima-media thickness and the presence of electrocardiographic left ventricular hypertrophy would be independently associated with increased ICH incidence. METHODS:Among 18,155 participants initially free of stroke in the Atherosclerosis Risk in Communities Study (ARIC) and the Cardiovascular Health Study (CHS), we assessed carotid intima-media thickness, carotid plaque, and electrocardiographic left ventricular hypertrophy. Over a median of 18 years of follow-up, 162 incident ICH events occurred. RESULTS:After adjustment for other ICH risk factors, carotid intima-media thickness was associated positively with incidence of ICH in both ARIC and CHS. The risk was lowest in study-specific Quartile 1, elevated 1.6- to 2.6-fold in Quartiles 2 to 3, and elevated 2.5 to 3.7-fold in Quartile 4 (P<0.05 for both studies). In CHS, having a carotid plaque was associated with a 2-fold (95% CI, 1.1-3.4) greater ICH risk than having no plaque, but only 1.2-fold (95% CI, 0.76-2.0) greater ICH risk in ARIC. Electrocardiographic left ventricular hypertrophy carried a hazard ratio of ICH of 1.7 (95% CI, 0.77-3.7) in CHS and 2.8 (95% CI, 1.2-6.4) in ARIC. CONCLUSIONS:Our data suggest that people with carotid atherosclerosis and possibly left ventricular hypertrophy are at increased risk not only of ischemic stroke, but also of ICH.
| S-EPMC3202073 | biostudies-literature
Project description:BackgroundElectrocardiogram (ECG) is commonly used clinically due to convenience, but its accuracy is insufficient for left ventricular hypertrophy (LVH) diagnosis. In this study, we attempted to improve diagnostic accuracy of LVH by establishing models with ECG parameters.MethodsEighty hundred and twenty eight patients were recruited in the present study which were divided into groups according to gender, age and body mass index (BMI). The sensitivity, specificity, Youden index, positive predictive value, negative predictive value and accuracy were calculated using ultrasonic cardiogram criteria of LVH as the gold standard. Area under the curve was also calculated to assess the diagnostic accuracy of 22 conventional ECG criteria in different groups. Stepwise discriminant analyses were performed to establish models of ECG for LVH.ResultsThe diagnostic accuracy of ECG11 (S V2 + R V5,6) and ECG12 (S V1,2 + R V5,6) was significantly higher than the other 20 criteria, while ECG15 (R V5/R V6) was lowest. The ECG12 sensitivity for males was 52.5%, for <60 years old was 44.2%, and for BMI <25 kg/m2 was 46.2%,higher than for females (27.5%), for ?60 years old (35.7%), and for BMI ?25 kg/m2(27.6%), respectively. The difference between genders was the most obvious. Based on these observations, the following models for males and females were established:[Formula: see text]and[Formula: see text]respectively. The sensitivities of the two new models were 71.4% and 75.8%, significantly higher than the22 conventional ECG criteria.ConclusionTwo models developed based on gender can be considered for use to investigate the preliminary assessment of the probability of LVH.
| S-EPMC5549337 | biostudies-literature
Project description:BackgroundLeft 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.MethodsThe 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).ResultsFrom 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.ConclusionsThe 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.
| S-EPMC10983697 | biostudies-literature
Project description:BackgroundIn hemodialysis patients, left ventricular hypertrophy (LVH) contributes to high cardiovascular mortality. We examined cardiovascular mortality prediction by the recently proposed Peguero-Lo Presti voltage since it identifies more patients with electrocardiographic (ECG) LVH than Cornell or Sokolow-Lyon voltages.MethodsA total of 308 patients on hemodialysis underwent 24 h ECG recordings. LVH parameters were measured before and after dialysis. The primary endpoint of cardiovascular mortality was recorded during a median 3-year follow up. Risk prediction was assessed by Cox regression, both unadjusted and adjusted for the Charlson Comorbidity Index and the Cardiovascular Mortality Risk Score.ResultsThe Peguero-Lo Presti voltage identified with 21% the most patients with positive LVH criteria. All voltages significantly increased during dialysis. Factors such as ultrafiltration rate, Kt/V, body mass index, sex, and phosphate were the most relevant for these changes. During follow-up, 26 cardiovascular deaths occurred. Post-dialysis Peguero-Lo Presti cut-off as well as the Peguero-Lo Presti and Cornell voltages were independently associated with cardiovascular mortality in unadjusted and adjusted analysis. The Sokolow-Lyon voltage was not significantly associated with mortality. An optimal cut-off for the prediction of cardiovascular mortality was estimated at 1.38 mV for the Peguero-Lo Presti.ConclusionsThe post-dialysis Peguero-Lo Presti cut-off as well as the Peguero-Lo Presti and Cornell voltages allowed independent risk prediction of cardiovascular mortality in hemodialysis patients. Measuring the ECG LVH parameters after dialysis might allow a standardized interpretation as dialysis-specific factors influence the voltages.
| S-EPMC8803820 | biostudies-literature
Project description:AimsDetermining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation.Methods and resultsWe chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM.ConclusionThese results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.
| S-EPMC9707995 | biostudies-literature