&lt;p&gt;Prediction of Left Ventricular Mass Index Using Electrocardiography in Essential Hypertension – A Multiple Linear Regression Model&lt;/p&gt;

Ahmed, Shah Newaz; Jhaj, Ratinder; Sadasivam, Balakrishnan; Joshi, Rajnish

doi:10.2147/mder.s253792

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Les données de la littérature ont relevé une corrélation positive entre d'une part la surface corporelle et d'autre part la masse ventriculaire du coeur au plan anatomique (Xue et al, 2019 ;Zhang et al, 2019) et au plan échocardiographique (référence pour la mesure directe de la masse ventriculaire gauche) (Lang et al, 2005 ;Ristow et al, 2010 ;Pfaffenberger et al, 2013 ;Cohen et al, 2017 ;Ahmed et al, 2020). Le calcul des IVVG représente une approche indirecte de mesure de la masse ventriculaire gauche à l'ECG.…”

Section: Discussionunclassified

“…Contrairement aux indices de Sokolow-Lyon et R en aVL l'indice et le produit de Cornell explorent la masse du ventricule gauche simultanément dans les deux plans frontal et horizontal (Taboulet, 2020). Ces indices ont une forte corrélation échocardiographique avec la masse ventriculaire gauche indexée à la surface corporelle (Lang et al, 2005 ;Ristow et al, 2010 ;Pfaffenberger et al, 2013 ;Cohen et al, 2017 ;Ahmed et al, 2020). Cependant, tout comme l'indice de Sokolow-Lyon et l'indice R en aVL, l'indice et le produit de Cornell demeurent une approche indirecte de mesure de la masse ventriculaire gauche qui pourrait être influencés par l'amplitude de la respiration et la position du coeur dans le médiastin (Swenson et al, 2011).…”

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Influence de la surface corporelle sur les indices de voltage ventriculaire gauche chez des sédentaires sains

Brou¹,

Balayssac-Siransy²,

Djaha-Toumata³

et al. 2022

Int. J. Bio. Chem. Sci

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Les Indices de Voltage Ventriculaire Gauche (IVVG) électrocardiographiques sont reconnus pour avoir une faible sensibilité dans le diagnostic des anomalies de la masse ventriculaire gauche (MVG) comparativement à l’échocardiographie, méthode de référence, dont les mesures sont indexées à la Surface Corporelle (SC). Or, ces IVVG qui estiment la MVG à l’électrocardiographie ne sont pas en pratique indexés à la SC. La non-indexation des IVVG à la SC serait-elle à l’origine de leur faible sensibilité ? L’objectif de cette étude était de déterminer l’influence de la SC sur les IVVG dans une population de sujets sédentaires sains. Une étude expérimentale, prospective incluant 117 volontaires de 18 à 35 ans a été réalisée. Ils ont été reçus le matin à jeun. La prise du poids et de la taille ont servi au calcul de la SC. Un électrocardiogramme (ECG) standard a été réalisé au repos. L’amplitude des ondes R et S et la durée du complexe QRS ont été mesurées. Les indices de Sokolow-Lyon, R en aVL, de Cornell et le produit de Cornell ont été calculés puis indexés à la SC. Aucune corrélation significative n’a été obtenue entre les IVVG étudiés et la SC. L’indexation des IVVG à la SC n’a pas modifié la différence observée entre le groupe de sujets à valeurs normales non indexées et celui à valeurs élevées non indexées. Les IVVG n’ont pas été influencés par la SC dans cette étude. Une approche incluant à la fois la mesure de la SC et celle du tour de buste pourrait aider à la recherche sur l’amélioration de l’apport diagnostique des IVVG. English title: Influence of body surface area on left ventricular voltage indices in healthy sedentary subjects Electrocardiographic Left Ventricular Voltage Indices (LVVI) are known to have low sensitivity in the diagnosis of left ventricular mass (LVM) abnormalities compared with echocardiography, the reference method, whose measurements are indexed to body surface area (BSA). However, these LVTI that estimate LVM on electrocardiography are not in practice indexed to BSA. Could the non-indexation of LVTI to BSA be the cause of their low sensitivity? The objective of this study was to determine the influence of BSA on LVVI in a population of healthy sedentary subjects. This was an experimental, prospective study involving 117 volunteers aged 18 to 35 years. They were received in the morning on an empty stomach. Weight and height measurements were used to calculate the BSA. A standard electrocardiogram was performed at rest. R and S wave amplitude and QRS duration were measured. Sokolow-Lyon, R in aVL, Cornell index, and Cornell product were calculated and indexed to BSA. No significant correlation was obtained between the studied LVVI and BSA. Indexation of LVVI to BSA did not alter the observed difference between the non-indexed normal and non-indexed high group. LVTI were not influenced by BSA. An approach that includes both BSA and torso circumference measurement could aid research on improving the diagnostic contribution of LVTI.

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Section: Discussionunclassified

Influence de la surface corporelle sur les indices de voltage ventriculaire gauche chez des sédentaires sains

Brou¹,

Balayssac-Siransy²,

Djaha-Toumata³

et al. 2022

Int. J. Bio. Chem. Sci

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“…(1) Select variables to establish a multiple linear regression model [36], let y represent the missing value of resistance. The data of drug resistance in other years were represented as x 1 , x 2 , .…”

Section: Data Preprocessingmentioning

confidence: 99%

A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model

Zhang

et al. 2021

Antibiotics

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There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-E. coli drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of E. coli, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R2 of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.

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Reversal of hypertensive heart disease: a multiple linear regression model

Ahmed¹,

Jhaj²,

Sadasivam³

et al. 2021

Discoveries

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Background: The development of left ventricular hypertrophy in primary hypertension increases cardiovascular mortality and morbidity. Reversal of left ventricular hypertrophy through therapeutic control of blood pressure reduces the risk of adverse cardiovascular incidents. Objective: In our study, we explored for the determinants of left ventricular hypertrophy regression. Methods: A cohort (n=217) of patients with hypertensive left ventricular hypertrophy was identified by screening consecutive patients in medical outpatient unit. The primary inclusion criteria were (i) Blood pressure more than140/90 mm of Hg (ii) Left Ventricular Mass Index more than 115 and 95 gm/m2 for males and females respectively. Left Ventricular Mass Index was determined by echocardiography at the time of recruitment and after 24 weeks of standard pharmacotherapy. The change in Left Ventricular Mass Index was modelled using multiple linear regression with both categorical and continuous explanatory variables. The effect of drug therapy on change in Left Ventricular Mass Index was tested in the model with dummy coded variables for the treatment categories. Results: In simple linear regression, the variables significantly correlating with change in Left Ventricular Mass Index were baseline Left Ventricular Mass Index (r=0.62, p<0.001), change in systolic blood pressure (r=0.22, p=0.001), change in mean blood pressure (r=0.16, p=0.02), baseline systolic blood pressure (r=0.15, p=0.02), age (r=0.12, p=0.09) and diabetes (r=0.12, p=0.09). The best fit model (r2=0.408) retained baseline Left Ventricular Mass Index (β=0.59, p<0.001), change in systolic blood pressure (β=0.14, p=0.01) and diabetes (β=-0.104, p=0.05) as the significant predictors. Introduction of treatment effect into the model non-significantly increased the fit of the model (r2=0.414, p=0.27-0.98). Conclusions: Pre-treatment Left Ventricular Mass Index and reduction in systolic blood pressure were the major determinants of left ventricular hypertrophy regression. We also observed that there is lesser left ventricular hypertrophy regression in diabetic patients, warranting future research to explore glycaemic control as a modifiable factor in left ventricular hypertrophy reversal.

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<p>Prediction of Left Ventricular Mass Index Using Electrocardiography in Essential Hypertension – A Multiple Linear Regression Model</p>

Cited by 3 publications

References 22 publications

Influence de la surface corporelle sur les indices de voltage ventriculaire gauche chez des sédentaires sains

Influence de la surface corporelle sur les indices de voltage ventriculaire gauche chez des sédentaires sains

A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model

Reversal of hypertensive heart disease: a multiple linear regression model

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