A Novel MRI-Based Lumbar Subcutaneous Adipose Classification for Predicting Surgical Site Infection and Adverse Events after Lumbar Fusion

Study DesignRetrospective cohort study.ObjectiveTo establish a novel lumbar subcutaneous adipose classification (LSAC) based on sagittal MRI and determine its predictive value for surgical site infection (SSI) and postoperative adverse events (PAE) following posterior lumbar fusion.MethodsA retrospective analysis of 1122 patients undergoing posterior lumbar interbody fusion was performed. Lumbar subcutaneous adipose distribution at the L3 endplate level was categorized into five types (Low, Cranial, Caudal, Cranial-Caudal, and Diffuse). Surgical segment subcutaneous fat thickness (SFT), facet joint to lumbar dorsal fascia distance (FJ-LDF), paraspinal muscle cross-sectional area, paraspinal muscle fat infiltration, and Modic changes were assessed. Associations between LSAC and SSI/PAE were examined using multivariate logistic regression.ResultsLSAC demonstrated excellent inter- and intra-observer reliability. Significant differences in patient characteristics were observed among the five types, with type 5 and type 2 showing higher BMI, diabetes and spondylolisthesis rate. SSI and PAE incidence were highest in type 5, followed by type 2. Multivariate analyses identified LSAC (OR 1.534, <i>P</i> = 0.007), BMI (OR 1.156, <i>P</i> = 0.010), transfusion (OR 1.002, <i>P</i> < 0.001), and smoking (OR 2.646, <i>P</i> = 0.035) as independent predictors of SSI; and LSAC (OR 1.477, <i>P</i> = 0.002), age (OR 1.037, <i>P</i> = 0.016), smoking (OR 2.607, <i>P</i> = 0.007), and higher drainage volume (OR 1.001, <i>P</i> < 0.001) as predictors of PAE. ROC curve demonstrated that LSAC was more sensitive than BMI and SFT to predict the SSI and PAE.ConclusionThe LSAC system provides a simple, reproducible method for quantifying lumbar subcutaneous fat distribution and outperforms traditional single adiposity indices such as BMI or SFT in predicting postoperative complications. Type 5 and type 2 represent high-risk phenotypes requiring enhanced perioperative optimization and surveillance.