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Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Relationship between left ventricular shape and cardiovascular risk factors: comparison between the Multi-Ethnic
Avan Suinesiaputra1,2, Kathleen Gilbert2,3, Charlene Mauger1,2
1Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.
Insights
Statistical shape atlases show generalizable left ventricular (LV) shape findings for diabetes and hypercholesterolemia across cohorts. However, obesity shows significant differences, possibly due to varying relationships with heart shape between populations.
Area of Science:
- Cardiovascular Imaging
- Medical Statistics
- Population Health
Background:
- Statistical shape atlases are crucial for large-cohort studies linking heart shape to cardiovascular risk factors.
- The generalizability of these shape-risk relationships across different populations remains largely unknown.
- This study addresses the unknown generalizability of left ventricular (LV) shape findings between cohorts.
Purpose of the Study:
- To compare left ventricular (LV) shapes in patients with differing cardiovascular risk factor profiles from two distinct cohorts.
- To investigate the direct usability of LV shape scores derived from one cohort in analyzing shape differences within another cohort.
- To assess the generalizability of statistical shape atlases in cross-cohort cardiovascular research.
Main Methods:
- Utilized two cardiac MRI cohorts: Multi-Ethnic Study of Atherosclerosis (MESA) with 2106 participants and UK Biobank (UKB) with 2960 participants.
- Constructed 3D LV shape atlases from expert-drawn contours from separate core labs.
- Assessed atlas generalizability by comparing receiver operating characteristic curve areas (AUC) for internal versus external validation (p>0.05).
Main Results:
- Significant differences in LV mass and volume indices were observed between cohorts, even in matched controls, potentially due to differing core lab protocols.
- The UKB atlas showed non-significant differences in discriminative performance for hypertension, diabetes, hypercholesterolemia, and smoking between internal and external validation.
- The MESA atlas also showed non-significant differences for diabetes and hypercholesterolemia, but both atlases revealed significant differences for obesity.
Conclusions:
- The MESA and UKB atlases demonstrated good generalizability for diabetes and hypercholesterolemia, indicating their utility without mass/volume corrections.
- Significant differences observed for obesity suggest varying relationships between obesity and LV shape across different populations.
- These findings highlight the potential and limitations of cross-cohort generalizability for statistical shape atlases in cardiovascular research.
Background:
Statistical shape atlases have been used in large-cohort studies to investigate relationships between heart shape and risk factors. The generalisability of these relationships between cohorts is unknown. The aims of this study were to compare left ventricular (LV) shapes in patients with differing cardiovascular risk factor profiles from two cohorts and to investigate whether LV shape scores generated with respect to a reference cohort can be directly used to study shape differences in another cohort.
Methods:
Two cardiac MRI cohorts were included: 2106 participants (median age: 65 years, 54% women) from the Multi-Ethnic Study of Atherosclerosis (MESA) and 2960 participants (median age: 64 years, 52% women) from the UK Biobank (UKB) study. LV shape atlases were constructed from 3D LV models derived from expert-drawn contours from separate core labs. Atlases were considered generalisable for a risk factor if the area under the receiver operating characteristic curves (AUC) were not significantly different (p>0.05) between internal (within-cohort) and external (cross-cohort) cases.
Results:
LV mass and volume indices were differed significantly between cohorts, even in age-matched and sex-matched cases without risk factors, partly reflecting different core lab analysis protocols. For the UKB atlas, internal and external discriminative performance were not significantly different for hypertension (AUC: 0.77 vs 0.76, p=0.37), diabetes (AUC: 0.79 vs 0.77, p=0.48), hypercholesterolaemia (AUC: 0.76 vs 0.79, p=0.38) and smoking (AUC: 0.69 vs 0.67, p=0.18). For the MESA atlas, diabetes (AUC: 0.79 vs 0.74, p=0.09) and hypercholesterolaemia (AUC: 0.75 vs 0.70, p=0.10) were not significantly different. Both atlases showed significant differences for obesity.
Conclusions:
The MESA and UKB atlases demonstrated good generalisability for diabetes and hypercholesterolaemia, without requiring corrections for differences in mass and volume. Significant differences in obesity may be due to different relationships between obesity and heart shapes between cohorts.
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