The evaluation of lumbar paraspinal muscle quantity and quality using the Goutallier classification and lumbar indentation value.

PURPOSE: The cross-sectional area and fat infiltration are accepted as standard parameters for quantitative and qualitative evaluation of muscle degeneration. However, they are time-consuming, which prevents them from being used in a clinical setting. The aim of this study was to analyze the relationship between lumbar muscle degeneration and spinal degenerative disorders, using lumbar indentation value (LIV) as quantitative and Goutallier classification as qualitative measures.

METHODS: This is a retrospective analysis of kinematic magnetic resonance images (kMRI). Two-hundred and thirty patients with kMRIs taken in weight-bearing positions were selected randomly. The LIV and Goutallier classification were evaluated at L4-5. The correlation of these two parameters with patients' age, gender, lumbar lordosis (LL), range of motion, disc degeneration, disc height, and Modic change were analyzed.

RESULTS: There was no significant trend of LIV among the different grades of Goutallier classification (p = 0.943). There was a significant increase in age with higher grades of Goutallier classification (p < 0.001). In contrast, there was no correlation between LIV and age (p = 0.799). The Goutallier classification positively correlated with LL (r = 0.377) and severe disc degeneration (r = 0.249). The LIV positively correlated with LL (r = 0.476) and degenerative spondylolisthesis (r = 0.184). Multinomial logistic regression analysis showed that age (p = 0.026), gender (p = 0.003), and LIV (p < 0.001) were significant predictors for patients with low LL (< 10°).

CONCLUSION: Lumbar muscle quantity and quality showed specific correlation with age and spine disorders. Additionally, LL can be predicted by the muscle quantity, but not the quality. These time-saving evaluation tools potentially accelerate the study of lumbar muscles. These slides can be retrieved under Electronic Supplementary Material.