Role of Paraspinal Muscle Degeneration as a Predictor of Chronic Low Back Pain in Lumbar Spondylolisthesis: A Machine Learning Model is Developed.

Abstract

Study DesignA Retrospective real-world study.ObjectiveThis study aimed to investigate whether paraspinal muscle degeneration (PMD) is a predictor of chronic low back pain (CLBP) in elderly patients with lumbar spondylolisthesis after posterior lumbar interbody fusion (PLIF).MethodsA comparative cohort analysis was performed on 86 patients aged >60 with Meyerding grade I-II spondylolisthesis. Patients were stratified into PMD and non-PMD groups based on psoas major muscle index (PMI) and multifidus fat infiltration (MFI). Clinical outcomes were assessed using Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores, alongside lumbosacral sagittal parameters including lumbar lordosis (LL), pelvic tilt (PT), pelvic incidence (PI), PI-LL (PL) mismatch. Propensity score matching for listhesis degree and osteoporosis yielded 30 patients per group for analysis. Multivariate analysis identified risk factors for CLBP at 2 years, and a machine learning model was developed as a clinically applicable predictive tool based on an expanded cohort of 649 patients.ResultsThe PMD group demonstrated significantly worse outcomes at 2-year follow-up, with higher VAS (2.40 ± 1.55 vs 1.23 ± 1.48) and ODI scores (29.40 ± 6.83 vs 20.57 ± 6.38), and poorer sagittal alignment. PMI and MFI were identified as major risk factors for CLBP both pre- and postoperatively. The Random Forest Model achieved high accuracy (97.1%), sensitivity (95.9%), specificity (90.6%), and an F1 score (88.9%) in predicting postoperative CLBP.ConclusionsPMD significantly impacts both preoperative symptom severity and postoperative recovery. Assessment of PMD provides valuable prognostic information, and the established predictive model can aid in managing surgical expectations and guiding rehabilitation.