Forecasting the results of surgical treatment of patients with degenerative diseases of the lumbar spine depending on the state of paravertebral muscles
It is proved that the paravertebral muscles play an important role in the recovery of patients with degenerative diseases of the lumbar spine after treatment. Objective: to create an algorithm for predicting the results of surgical treatment of patients with degenerative diseases of the lumbar spine based on an assessment of the state of the paravertebral muscles before surgery. Methods: 74 patients were examined. They had surgeries because of instability of moving vertebral segments (15 patients), hernias of intervertebral disks (25), spondylolisthesis (15), spinal canal stenosis (19). Pain syndrome was assessed
using VAS, quality of life was assessed with Oswestry index. Patients underwent transpedicular fixation on one or two levels LIII, LIV, LV, SI. A CT examination was made and the content of adipose, muscular and connective tissues in the paravertebral
muscles of lumbar spine was determined using an original high accuracy computer software. Results: before the treatment 13 (17.6 %) patients had serious disabilities, 30 (40.5 %)
were disabled, and 31 (41.9 %) were clinging to the bed. Minor disabilities were observed in 24 patients (32.4 %), moderate — in 37 patients (50.0 %), serious and incapacitating ones remained in 13 patients (17.6 %) after the surgery. The importance of the signs for predicting treatment outcomes was evaluated with a 100-point scale. The most significant of these is the total fat content in the paravertebral muscles and connective tissue in m. erector spinae. It is followed by the content of muscle tissue in m. quadratus lumborum and connective tissue in m. multifidus. Conclusions: the correlation between the results of the surgical treatment of patients with degenerative diseases of the lumbar spine and the state of the paravertebral muscles has been proved. The main influencing factor is the content of adipose tissue in muscles: the higher it is, the worse is the result. The accuracy of the proposed algorithm is 89.19 %.
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