Correlation between Body Composition and Inter-Examiner Errors for Assessing Lumbar Multifidus Muscle Size, Shape and Quality Metrics with Ultrasound Imaging

Abstract

Ultrasound imaging (US) is widely used in several healthcare disciplines (including physiotherapy) for assessing multiple muscle metrics such as muscle morphology and quality. Since measuring instruments are required to demonstrate their reliability, accuracy, sensitivity, and specificity prior to their use in clinical and research settings, identifying factors affecting their diagnostic accuracy is essential. Since previous studies analyzed the impact of sociodemographic but not body composition characteristics in US errors, this study aimed to assess whether body composition metrics are correlated with ultrasound measurement errors. B-mode images of the lumbar multifidus muscle at the fifth lumbar vertebral level (L5) were acquired and analyzed in 49 healthy volunteers by two examiners (one experienced and one novel). Cross-sectional area, muscle perimeter and mean echo intensity were calculated bilaterally. A multivariate correlation matrix was calculated for assessing the inter-examiner differences with body composition metrics. Results demonstrated excellent reliability (intraclass correlation coefficient, ICC > 0.9) for assessing the muscle cross-sectional area and perimeter, and good reliability for assessing the muscle shape and mean echo intensity (ICC > 0.7). Inter-examiner errors for estimating muscle size were correlated with participants’ age (p value, p < 0.01), weight (p < 0.05), total and trunk lean mass (both, p < 0.01) and water volume (p < 0.05). Greater shape descriptors and mean brightness disagreements were correlated with older ages (p < 0.05) and total lean mass (p < 0.05). No correlations between age and body composition metrics were found (p > 0.05). This study found US to be a reliable tool for assessing muscle size, shape and mean brightness. Although aging showed no correlations with body composition changes in this sample, it was the main factor correlated with US measurement errors.