A Novel Magnetic Resonance Imaging-based Lumbar Muscle Grade to Predict Health-related Quality of Life Scores Among Patients Requiring Surgery

Abstract

Study Design. Retrospective cross-sectional cohort. Objective. The aim of this sudy was to determine whether muscle health measurements are associated with health-related quality of life scores (HRQOLs) for patients with lumbar spine pathology. Summary of Background Data. Poor muscle health has been implicated as a source of pain/dysfunction for patients with lumbar spine pathology. Our aim was to quantify the relationship using muscle health measurements and HRQOLs. Methods. Three hundred and eight patients were included (mean age 57.7 ± standard deviation 18.2 years’ old). We randomly selected patients into a derivation cohort (200) and validation cohort (108) to create our muscle health grade. We measured muscle health by the lumbar indentation value (LIV), goutallier classification (GC), and ratio of paralumbar muscle cross-sectional area over body mass index (PL-CSA/BMI). A muscle health grade was derived based on whether a measurement showed a statistically significant impact on visual analog scale back and leg pain (VAS-leg and VAS-leg), Oswestry Disability Index (ODI), short-form 12 physical health score (SF-12 PHS), short-form 12 mental health score (SF-12 MHS) and Patient-reported Outcomes Measurement Information System (PROMIS). A variety of statistical tools were used to determine whether there was a relationship between a measurement and HRQOLs. Results. In the derivation cohort, a muscle health grade was created based on the GC and PL-CSA/BMI ratio. For patients with a GC ≤2, one point was given. For patients with a PL-CSA/BMI ≥130, one point was given. Patients with 2 points were graded as “A” and 0 or 1 point were graded “B.” Within the validation cohort of patients, there was a statistically significant higher PROMIS (mean 34.5 ± standard deviation 12.6 vs. 27.6 ± 14.0, P = 0.002), ODI (38.8 ± 18.3 vs. 45.8 ± 18.1, P = 0.05) and SF-12 PHS (34.7 ± 11.3 vs. 29.1 ± 6.3, P = 0.002) for patients with a good muscle health grade of “A.” Conclusion. This study offers an objective measurement of muscle health that correlates with HRQOLs for patients with lumbar spine pathology. Level of Evidence: 3

Paraspinal Muscle in Chronic Low Back Pain: Comparison Between Standard Parameters and Chemical Shift Encoding‐Based Water–Fat MRI

“…Imaging by CSE‐MRI is not routine in patients with chronic LBP, and the technique is not yet widely available. In the absence of CSE‐MRI acquisitions, other parameters have been used to estimate PSM characteristics on clinical routine T1‐ or T2‐weighted images in patients with chronic LBP 13–21 . Our data suggest that the GC is the most accurate method for assessing fatty infiltration of PSM, as the GC score significantly associated with the FF of PSM from CSE‐MRI.…”

“…12 Furthermore, a number of MRI-based studies have extracted parameters mainly from conventional axial T1or T2-weighted sequences, such as the muscle volume or cross-sectional area (CSA), signal intensity (SI), semiquantitative scoring using the Goutallier classification (GC), and recently, the lumbar indentation value (LIV). [13][14][15][16][17][18][19][20][21] Muscle volume, CSA, and LIV are measures of muscle geometry or size that may show alterations in patients with LBP. 16,[19][20][21] In contrast, the semiquantitative GC scoring system and the muscle-fat index (MFI), which is calculated by dividing the mean SI in a specific muscle by the SI in a homogenous region of fat outside the muscle, have been used to evaluate fatty degeneration of PSM and, thus, may reflect estimates of PSM quality in LBP.…”

“…For example, CT attenuation of the multifidus and erector spinae muscles from regions of interest (ROIs) placed in the center of the muscle mass have been compared between patients with and without LBP, but no significant differences have been found 12 . Furthermore, a number of MRI‐based studies have extracted parameters mainly from conventional axial T1‐ or T2‐weighted sequences, such as the muscle volume or cross‐sectional area (CSA), signal intensity (SI), semiquantitative scoring using the Goutallier classification (GC), and recently, the lumbar indentation value (LIV) 13–21 . Muscle volume, CSA, and LIV are measures of muscle geometry or size that may show alterations in patients with LBP 16,19–21 .…”

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

“…An accurate segmentation tool that does not require any human intervention can therefore act as the key enabling technology for large-scale studies. Sarcopenia, in particular in terms of reduced mass and quality of the back muscles, is associated with higher disability and lower quality of life in patients suffering from spinal disorders with respect to non-sarcopenic patients [18]. Sarcopenic patients subjected to spine surgery have a slower and sometimes less complete recovery, more severe postoperative symptoms and lower satisfaction, whereas the correlation between sarcopenia and increased risk of postoperative complications could not be proved [19].…”

Age- and sex-dependent differences in the morphology and composition of paraspinal muscles between subjects with and without lumbar degenerative diseases

“…The image-processing software platform (Image J, National Institutes of Health, Bethesda, MD) was used to trace out the boundary of the multifidus, erector spinae, and psoas (Fig. 1 ) [ 15 , 18 ]. FI% was calculated using threshold technique [ 8 ].…”