Defining echocardiographic reference values of LV volume indices and biventricular strain in obese patients with normal ejection fraction in different cardiac remodeling patterns – a single center study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Badawy ◽  
R Jadav ◽  
M Anastasius ◽  
V Jain ◽  
A Zahid ◽  
...  
Keyword(s):  
Ejection Fraction ◽  
Reference Values ◽  
Cardiac Remodeling ◽  
Normal Ejection Fraction ◽  
Obese Patients ◽  
Lv Remodeling ◽  
Obese Subjects ◽  
Lv Volumes ◽  
Lv Volume ◽  
Remodeling Pattern

Abstract Background The left ventricle (LV) in obese patients undergoes different patterns of remodeling in order to normalize wall stress. However, little is known about how LV volume indices, LV global longitudinal strain and right ventricular free wall strain (GLS) vary according to the pattern of LV remodeling. Aim To define the echocardiographic reference values of LV volumes and biventricular GLS across the different LV remodeling patterns in obese patients with a preserved ejection fraction. Methods 2393 adult obese patients (1428 females, 965 males) with a normal ejection fraction who underwent echocardiography from January 2008 to December 2018 were selected. They were categorized according to 4 cardiac remodeling groups defined by LV mass index (102g/m2 in males, 88g/m2 in females) and relative ventricular wall thickness (0.42): normal geometry (NG), eccentric hypertrophy (EH), concentric remodeling (CR) and concentric hypertrophy (CH). Obese subjects were further categorized by BMI class (30–35, 35–40, >40 kg/m2). Obese subjects were gender matched to controls with a normal BMI (18.5–25 kg/m2) and normal cardiac geometry. Mean ± SD, One-way Anova and Tukey- Kramer HSD were applied. P<0.05 is considered significant. Results The mean age of controls and obese patients' were 50±16 and 57±13.6 years respectively (P<0.0001). LV GLS for controls compared to obese subjects with NG, EH, CR and CH was −21.1±2 vs. −20.2±1.9, −19.6±2.8, −18.5±2.9, −17.5±3.4 respectively (p<0.0001 for all), and for RV GLS it was −27.9±4 vs −26.7±3.9, −25.1±5, −23.5±5.5, −24.1±5.2 respectively (p<0.01 for all, except for NG where p=0.2). The distribution of LV indices according to cardiac remodeling subtypes is shown in the figure. Indexed end diastolic and end systolic volumes were smaller in NG, CH and CR compared to controls (p<0.001 for each respectively). LV GLS and ejection fraction were higher in females, while indexed LV volumes were higher in males within each remodeling category (P<0.0001). No significant difference in LV GLS or indexed LV volume was seen across BMI categories within each remodeling pattern (P>0.05). Obese subjects with CH had the highest incidence of the cardiovascular risk factors hyperlipidemia, hypertension and history of myocardial infarction or stroke, compared to those with other remodeling patterns (p<0.0001 for each, vs. NG, EH and CR). Conclusion To our knowledge, this is the largest study to define LV volumes and left and right ventricular GLS according to LV remodeling pattern and BMI category. The Lowest GLS was noted in CH. Ejection fraction was similar across the LV remodeling patterns. There were no differences in GLS and LV indexed volumes across BMI categories within each remodeling group. These results can be applied as a reference values for the obese population with a normal LV ejection fraction. Funding Acknowledgement Type of funding source: None

scholarly journals Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert

2019 ◽  
Vol 12 (9) ◽  
Author(s):  
Federico M. Asch ◽  
Nicolas Poilvert ◽  
Theodore Abraham ◽  
Madeline Jankowski ◽  
Jayne Cleve ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ejection Fraction ◽  
Reference Values ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Machine Learning Algorithm ◽  
Mean Absolute Deviation ◽  
Limits Of Agreement ◽  
Absolute Deviation ◽  
Lv Volumes

Background: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. Methods: Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. Results: Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r =0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians’ measurements: r =0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87. Conclusions: Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.

scholarly journals Population-Based Reference Values for 3D Echocardiographic LV Volumes and Ejection Fraction

2012 ◽  
Vol 5 (12) ◽  
pp. 1191-1197 ◽  
Author(s):  
Navtej S. Chahal ◽  
Tiong K. Lim ◽  
Piyush Jain ◽  
John C. Chambers ◽  
Jaspal S. Kooner ◽  
...  
Keyword(s):  
Ejection Fraction ◽  
Reference Values ◽  
Population Based ◽  
Lv Volumes

P2446Defining values of volumes and longitudinal strain for the different types of left ventricular remodeling patterns in subjects with normal ejection fraction - a single center study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Jadav ◽  
L Nhola ◽  
J Thaden ◽  
P Pellikka ◽  
P Pislaru ◽  
...  
Keyword(s):  
Ejection Fraction ◽  
Body Surface ◽  
Longitudinal Strain ◽  
Normal Ejection Fraction ◽  
Single Center ◽  
Concentric Hypertrophy ◽  
Single Center Study ◽  
Different Types ◽  
Lv Volumes ◽  
Center Study

Abstract Background The left ventricle (LV) undergoes different types of remodeling to normalize its wall stress. There has been an increase in the trend in research regarding the process of remodeling but little is known about how LV volumes and global longitudinal strain (GLS) may vary depending upon the type of remodeling. Purpose To define the values of LV volumes and GLS in patients with different types of LV remodeling with normal ejection fraction (EF). Methods Single center retrospective study conducted from 2008 to 2018. We selected 10,356 subjects >18 years (5808 females, 4548 males) with normal EF and who underwent 2D echocardiography (2DE) at Mayo clinic, Rochester. They were categorized into 4 groups (Figure) i.e., group 1 = normal geometry, group 2 = concentric remodeling, group 3 = concentric hypertrophy, group 4 = eccentric hypertrophy based on their LV mass index (102 g/m2 in males, 88 g/m2 in females) and relative wall thickness (0.42) values measured by 2DE (figure). The patterns of LV GLS, end diastolic and end systolic volumes (EDV, ESV) indexed to body surface area were analyzed in these four groups by gender and body mass index (BMI). Mean±SD, One-way Anova, two sample t-test and Tukey- Kramer HSD were applied. Results The mean age of the selected population is 57.9 years ±14.9 (females=57.7±14.7, males=58.2±15.1) and the body surface area of 1.9 m2±0.2 (females=1.8±2 and males=2.1±0.2). The LV GLS values (Figure) of groups 1, 2, 3 and 4 were −20.7, −18.9, −17.5 and −19.9 and their EDV cc/m2 values were 63.6, 58.5, 61.2 and 68.2 respectively. LV GLS and Biplane EF values were higher in females while Biplane EDV and ESV were higher in males. When each gender is sub-divided based on their BMI (<25 and ≥25), the LV GLS, EDV and ESV values were higher in all groups with BMI<25 except in males with concentric remodeling and females with concentric hypertrophy where they were low. Remodeling distribution Conclusion To our knowledge, this is the biggest single center study to evaluate LV GLS and volumes based on type of remodeling by gender and BMI. When compared with normal geometry, LV volumes and GLS values were statistically different among each type of remodeling as well as for gender and BMI. These results can be used as reference values in patient population studies where remodeling is being analyzed in subjects with normal LV EF. Acknowledgement/Funding None

P4347Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F M Asch ◽  
N Poilvert ◽  
T Abraham ◽  
M Jankowski ◽  
J Cleve ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ejection Fraction ◽  
Reference Values ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Machine Learning Algorithm ◽  
Computer Algorithms ◽  
Human Expert ◽  
Lv Volumes

Abstract Background Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by standard calculation of model-based end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could be developed, which circumvents border detection and instead estimates the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. Purpose This study was designed to test the feasibility and accuracy of this approach. Methods Machine learning algorithm was developed and trained on a database of >50,000 echocardiographic studies, including multiple apical 2- and 4-chamber views, to automatically estimate LVEF (AutoEF, BayLabs). Testing was performed on an independent group of 99 unselected patients, whose automated EF values were compared to reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis of bias and limits of agreement (LOA). Consistency was assessed by mean absolute deviation (MAD) among automated estimates based on different combinations of apical views. Finally, sensitivity and specificity of detecting of EF≤35% was calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. Results Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (MAD=2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, LOA=±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, LOA=±13.4%,sensitivity 0.93, specificity 0.87. Conclusions Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared to reference values provided by an expert panel. Acknowledgement/Funding Bay Labs, Inc.

Effects of Rest and Exercise on Cardiac Blood Volume Determinations

1997 ◽  
Vol 36 (08) ◽  
pp. 259-264
Author(s):  
N. Topuzović
Keyword(s):  
Radionuclide Ventriculography ◽  
Left Ventricular ◽  
Peak Exercise ◽  
Volume Determination ◽  
Group I ◽  
Lv Volumes ◽  
Group Ii ◽  
Lv Volume

Summary Aim: The purpose of this study was to investigate the changes in blood activity during rest, exercise and recovery, and to assess its influence on left ventricular (LV) volume determination using the count-based method requiring blood sampling. Methods: Forty-four patients underwent rest-stress radionuclide ventriculography; Tc-99m-human serum albumin was used in 13 patients (Group I), red blood cells was labeled using Tc-99m in 17 patients (Group II) in vivo, and in 14 patients (Group III) by modified in vivo/in vitro method. LV volumes were determined by a count-based method using corrected count rate in blood samples obtained during rest, peak exercise and after recovery. Results: In group I at stress, the blood activity decreased by 12.6 ± 5.4%, p <0.05, as compared to the rest level, and increased by 25.1 ± 6.4%, p <0.001, and 12.8 ± 4.5%, p <0.05, above the resting level in group II and III, respectively. This had profound effects on LV volume determinations if only one rest blood aliquot was used: during exercise, the LV volumes significantly decreased by 22.1 ± 9.6%, p <0.05, in group I, whereas in groups II and III it was significantly overestimated by 32.1 ± 10.3%, p <0.001, and 10.7 ± 6.4%, p <0.05, respectively. The changes in blood activity between stress and recovery were not significantly different for any of the groups. Conclusion: The use of only a single blood sample as volume aliquot at rest in rest-stress studies leads to erroneous estimation of cardiac volumes due to significant changes in blood radioactivity during exercise and recovery.

Echocardiographic Evaluation of Cardiac Remodeling After Surgical Closure of Ventricular Septal Defect in Different Age Group

2017 ◽  
Vol 2 (2) ◽  
pp. 69-74
Author(s):  
Mohammad Aminullah ◽  
Fahmida Akter Rima ◽  
Asraful Hoque ◽  
Mokhlesur Rahman Sazal ◽  
Prodip Biswas ◽  
...  
Keyword(s):  
Ventricular Septal Defect ◽  
Ejection Fraction ◽  
Cardiac Remodeling ◽  
Septal Defect ◽  
Left Ventricular ◽  
Age Group ◽  
Surgical Closure ◽  
Group A ◽  
Group B ◽  
Fractional Shortening

Background: Cardiac remodeling is important issue after surgical closure of ventricular septal defect.Objective: The purpose of the present study was to evaluate cardiac remodeling by echocardiography by measuring the ejection fraction, fractional shortening, left ventricular internal diameter during diastole (LVIDd) and left ventricular internal diameter during systole (LVIDs) after surgical closure of ventricular septal defect in different age group. Methodology: This prospective cohort studies was conducted in the Department of Cardiac Surgery at National Institute of Cardiovascular Disease (NICVD), Dhaka. Patient with surgical closure of VSD were enrolled into this study purposively and were divided into 3 groups according to the age. In group A (n=10), patients were within the age group of 2.0 to 6.0 years; age of group B (n=8) patients were 6.1-18.0 years and the group C (n=6) aged range was 18.1-42.0 years. Echocardiographic variables such as ejection fraction, fractional shortening, LVIDd, LVIDs were taken preoperatively and at 1st and 3rd month of postoperative values. Result: A total number of 24 patients was recruited for this study. The mean ages of all groups were 12.60±12.09. After 1 month ejection fraction were decreased by 5.97%, 6.71% and 5.66% in group A, group B and group C respectively. After 3 months ejection fraction were increased by 6.13%, 5.13% and 5.14% in group A, group B and group C respectively. After 1 month fractional shortening were decreased by 13.55%, 9.30% and 9.09% in group A, group B and group C respectively. After 3 months fractional shortening were increased by 7.23%, 7.35% and 4.55% in group A, group B and group C respectively. After 1 month LVIDd were increased by 1.97%, 1.91% and 1.32% in group A, group B and group C respectively. After 3 months LVIDd were decreased by 10.84%, 9.89% and 7.34% in group A, group B and group C respectively. After 1 month LVIDs were increased by 2.19%, 2.86% and 1.98% in group A, group B and group C respectively. After 3 months LVIDs were decreased by 11.68%, 10.97% and 8.87% in group A, group B and group C respectively.Conclusion: Cardiac remodeling occurred after surgical closure of ventricular septal defect and remodeling were more significant in younger age group. Journal of National Institute of Neurosciences Bangladesh, 2016;2(2):69-74

Sign in / Sign up

Export Citation Format

Share Document