scholarly journals Sensitivity and Specificity of an Electrocardiogram to Detect Echocardiographic Left Ventricular Hypertrophy in a Sample of 326 Tanzanian Adults: Differences in Men and Women

2021 ◽  
Vol Volume 12 ◽  
pp. 23-31
Author(s):  
Pilly Chillo
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Men And Women

Abstract 17246: Sensitivity and Specificity of Relative Sparing of Apical Longitudinal Strain for Detection of Systemic Light-Chain Amyloidosis versus Transthyretin Amyloidosis

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Michael Jiang ◽  
Julia M Simkowski ◽  
Nadia El Hangouche ◽  
Jeesoo Lee ◽  
Milica Marion ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Light Chain ◽  
Longitudinal Strain ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Control Group ◽  
Transthyretin Amyloidosis ◽  
Light Chain Amyloidosis ◽  
Significant Difference

Introduction: Relative apical sparing of longitudinal strain (RALS, the ratio of apical strain vs the rest of the heart) on echocardiography has been found to have high sensitivity and specificity for differentiating cardiac amyloidosis (CA) from other causes of left ventricular hypertrophy. Previous studies have shown no significant difference between amyloid subtypes, systemic light-chain amyloidosis (AL) and transthyretin amyloidosis (ATTR) Hypothesis: There will be a significant difference in sensitivity and specificity of RALS to detect CA across amyloid subtypes. Methods: A cohort of patients with either AL or ATTR amyloid was identified, with a control cohort of patients with left ventricular hypertrophy (LVH) of other etiologies. Speckle tracking echocardiography was performed on EchoPAC (GE Medical Systems) software to obtain values of basal, mid, and apical longitudinal strain for each patient; relative apical strain was then calculated. Results: The TTR group (n=22) was older (66.4±7.9, 76.6±11.6, p=0.001) and more likely to be female (p=0.009) than the AL group (n=30), both groups had similar rates of hypertension, diabetes mellitus, and end stage renal disease. Echocardiographic markers of diastolic function were decreased in both groups; the AL group had decreased left ventricle end diastolic volume (60.9±25.5, 94.9±50.2, p=0.012) and mean wall thickness (1.4±0.3, 1.6±0.4 p=0.017). ROC analysis using a RALS cutoff of 2 to differentiate AL and ATTR from the LVH control group revealed similar specificity (AL 85%, ATTR 85%) and sensitivity (AL 40%, ATTR 50%). Difference in area-under-curve (AUC) was not significant (p=0.2) (figure). Conclusions: ATTR and AL amyloid have similar specificity, but ATTR has a trend towards improved sensitivity over AL for detection of CA using RALS with the previously validated threshold of 2. This might become significant with a larger sample, work that is currently on-going..

P311Hypertrophic cardiomyopathy and other forms of left ventricular hypertrophy. The P wave can make the difference

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
S Raab ◽  
L Roten ◽  
M Branca ◽  
N Nozica ◽  
M Wilhelm ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Scoring System ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
P Wave ◽  
Predictive Score ◽  
Youden Index ◽  
P Value ◽  
Athletes Heart

Abstract Background Structural disarray of hypertrophied myocytes and interstitial fibrosis characterize hypertrophic cardiomyopathy (HCM). These morphological changes also affect atrial myocytes and, together with hemodynamic alterations because of HCM, may lead to atrial cardiomyopathy.  Purpose To investigate the incremental value of P-wave parameters to differentiate left ventricular hypertrophy (LVH) because of HCM from LVH in hypertensive heart disease (HHD) and athletes heart.  Methods In a prospective study, we compared electrocardiographic (including signal-averaged ECG of the P wave) and echocardiographic data of patients with HCM, HHD and athletes heart. We developed a predictive model with a simple scoring system to identify HCM. Results We compared data of 27 patients with HCM (70% males, 49.8 ± 14.5 years), 324 patients with HHD (52% males, 74.8 ± 5.5 years), and 215 subjects with athletes heart (72% males, 42.3 ± 7.5). The table shows the significant differences among the 3 groups. We included the following parameters into a predictive score to differentiate HCM from other forms of LVH: QRS width (>88ms = 1 point), P-wave integral (>688µVs = 1 point) and septum thickness (>12mm = 2 points). A score >2 (Youden index 0.626) correctly classified HCM in 81% of the cases with a sensitivity and specificity of 82% an 81%, respectively.  Conclusion Differentiation of HCM from other forms of LVH is improved by including atrial parameters. A simple scoring system including septum thickness, QRS width and P wave integral allowed identification of patients with HCM with a sensitivity and specificity of >80%. This score needs to be validated prospectively. Table 1 HCM HHD Athletes P-value HCM vs HHD* HCM vs Athletes* 95%-CI P-value 95%-CI P-value P-wave duration [ms] 152.7 ± 25.8 143.9 ± 16.5 133.5 ± 14.2 <0.001 -16.9 -24.6 to -9.1 <0.001 -16.3 -22.7 to -9.9 <0.001 P-wave integral [µVs] 850.4 ± 272.4 672.0 ± 235.4 773.1 ± 260.1 <0.001 -198.6 -320.8 to -76.3 0.002 -68.2 -169.7 to 33.2 0.187 QRS [ms] 110.3 ± 27.3 96.9 ± 20.3 95.1 ± 9.8 <0.001 -16.4 -24.7 to -8.1 <0.001 -13.8 -20.8 to -6.9 <0.001 QTc [ms] 447.9 ± 27.2 438.6 ± 24.5 414.0 ± 22.9 <0.001 -21.1 -32.7 to -9.5 <0.001 -30.8 -40.5 to -21.2 <0.001 LVMMI [g/m2] 153.6 ± 55.5 133.5 ± 30.3 98.6 ± 19.7 <0.001 -15.3 -29.7 to -0.9 0.038 -56.1 -67.7 to -44.6 <0.001 IVS [ms] 16.8 ± 4.2 11.8 ± 2.2 10.3 ± 1.5 <0.001 -5.2 -6.3 to -4.1 <0.001 -6.4 -7.3 to -5.6 <0.001 LAVI [ml/m2] 43.2 ± 13.9 30.5 ± 9.7 30.8 ± 9.5 <0.001 -14.6 -20.0 to -9.3 <0.001 -12.2 -16.6 to -7.9 <0.001 The table shows the study result after univariate and multivariate (*; adjusting for age and sex) analysis. Abstract Figure 1

Abstract P253: Genome-Wide Association Study of Electrocardiographic Left Ventricular Hypertrophy among Persons of European Ancestry: the Kaiser Permanente/UCSF GERA Cohort

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Carlos Iribarren ◽  
Ling Shen ◽  
Alfred D Round ◽  
Edward McNulty ◽  
Peter M Okin ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Ventricular Hypertrophy ◽  
Genome Wide Association Study ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Kaiser Permanente ◽  
Men And Women ◽  
Genome Wide ◽  
Cornell Voltage

Electrocardiographic (ECG)-derived left ventricular hypertrophy (LVH) is a recognized phenotype of hypertensive target organ damage and an independent risk factor for cardiovascular disease morbidity and mortality, and has been shown to be 20 to 40% heritable. A major prior collaborative study (EchoGen) relied on echocardiography measurement of LVH. We report here the results of the first genome-wide association study of an ECG index of LVH, the Cornell voltage, in a large US, population-based cohort. The Kaiser Permanente/UCSF Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort enrolled 110,266 persons, of whom 84,760 (77%) were of European ancestry (EA). Twelve-lead ECGs (obtained as part of routine medical care) were available in 59% (n=52,423; 62% female, mean age ± SD, 62 ± 13 years) of EA members of the GERA cohort. For those with more than 1 ECG (75%), one was selected at random. We programmatically derived from the digital tracings the Cornell voltage as R in aVL plus S in V3 (in mV). Mean ± SD Cornell voltages were 1.39 ± 0.58 mV in men and 1.16 ± 0.55 mV in women. Genotypes were generated on 674,518 SNPs using a custom designed Affymetrix Axiom array, and imputation was conducted using the 1000 Genomes data as a reference set. We identified 10 genetic loci (nearest genes: NFIA, CRIM1, CCDC141, HAND1, VGLL2, CTNNA, TBX3, SIPA1L1, ZNF595, and PROCR) with genome-wide significant associations (p<5*10 -8 ) with Cornell voltage as a continuous variable. Five of the loci (NFIA, CRIM1, HAND1, TBX3, and SIPA1L1) have previously been reported to influence QRS and PR intervals, while the other loci are novel. Interestingly, none of these loci were associated with left ventricular mass in the EchoGen consortium. Sex-specific analyses supported the association of these loci in both men and women; six loci were associated at a genome-wide significant level in these stratified analyses, including TBX3 and ZNF595 in both men and women, CCDC141, SIPA1L1, and PROCR in men, and VGLL2 in women, despite reduced sample sizes. These results may help elucidate both the biological and genetic basis of this clinically relevant phenotype.

Depression, Cardiometabolic Function and Left Ventricular Hypertrophy in African Men and Women: The SABPA Study

2012 ◽  
Vol 35 (3) ◽  
pp. 213-219 ◽  
Author(s):  
Nyiko Mashele ◽  
Leoné Malan ◽  
Johannes M. van Rooyen ◽  
Brian H. Harvey ◽  
Johan C. Potgieter ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Men And Women ◽  
Cardiometabolic Function

scholarly journals Using deep learning method to identify left ventricular hypertrophy on echocardiography

2021 ◽  
Author(s):  
Xiang Yu ◽  
Xinxia Yao ◽  
Bifeng Wu ◽  
Hong Zhou ◽  
Shudong Xia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Ventricular Hypertrophy ◽  
Early Stage ◽  
Hypertensive Heart Disease ◽  
Complete Classification ◽  
Left Ventricular ◽  
Classification Model ◽  
Detection Model

Abstract Background Left ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH. Methods We retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities. Results In terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94–0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3–98.7%) and 91.6% (95% CI 84.6–96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82–0.95) for HCM, AUC = 0.94 (95% CI 0.88–0.98) for CA, and AUC = 0.88 (95% CI 0.80–0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%. Conclusion Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.

Electrocardiographic diagnosis of left ventricular hypertrophy: Influence of body build

1988 ◽  
Vol 75 (6) ◽  
pp. 589-592 ◽  
Author(s):  
James M. McLenachan ◽  
Esther Henderson ◽  
Karen I. Morris ◽  
Henry J. Dargie
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Diagnostic Performance ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Obese Patients ◽  
Hypertensive Patients ◽  
Body Build ◽  
Echocardiographic Study ◽  
Obese Subjects

1. The sensitivity and specificity of four sets of electrocardiographic criteria for detection of left ventricular hypertrophy were evaluated in an echocardiographic study of 100 hypertensive patients. 2. All criteria gave reasonable specificity (87–94%) but poor sensitivity (39–52%). 3. When non-obese and obese patients were studied separately, criteria based on chest lead voltages were more sensitive than limb lead criteria for detection of left ventricular hypertrophy in non-obese subjects; however, the reverse was true in obese hypertensive patients, where criteria based on limb lead voltages were more sensitive than chest lead voltage criteria. 4. These data suggest that stratification of subjects by body build might improve the diagnostic performance of the electrocardiogram for detection of left ventricular hypertrophy.

scholarly journals Determinants of sensitivity and specificity of electrocardiographic criteria for left ventricular hypertrophy.

Circulation ◽  
1990 ◽  
Vol 81 (3) ◽  
pp. 815-820 ◽  
Author(s):  
D Levy ◽  
S B Labib ◽  
K M Anderson ◽  
J C Christiansen ◽  
W B Kannel ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Sensitivity And Specificity ◽  
Ventricular Hypertrophy ◽  
Left Ventricular
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