Abstract

Previous studies have shown that bone mineral density (BMD) is a predictor of cage subsidence. Phantom-less quantitative computed tomography (PL-QCT) can measure volumetric bone mineral density (vBMD) of lumbar trabecular and cortical bone. The study of endplate vBMD (EP-vBMD) is important in predicting cage settlement after extreme lateral interbody fusion (XLIF). This study aimed to determine the risk factors for postoperative cage subsidence after XLIF, particularly focusing on the relationship between vBMD measured by automatic PL-QCT and cage subsidence. Patients who underwent XLIF surgery from January 2018 to October 2020 with a minimum of 6 months of follow-up were retrospectively included. Cage subsidence was defined as >2 mm cage sinking on the adjacent endplate in follow-up imaging evaluation. Outcome measures were localized vBMDs included EP-vBMDs with different region of interest (ROI) heights measured by PL-QCT based on a customized muscle-fat algorithm. Shapiro-Wilk test, one-way ANOVA, Mann-Whitney test, Fisher exact test, univariable and multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were executed in this study. One hundred and thirteen levels of 78 patients were included in the analysis. The mean age was 65 ± 7.9 years for 11 males and 67 females. Cage subsidence occurred on 45 (39.8%) surgical levels. There was no significant difference in demographics, fused levels, or preoperative radiographic parameters. 1.25-mm EP-vBMD (0.991 [0.985,0.997], p = 0.004) and P-TB-vBMD (cage-positioned trabecular volumetric bone mineral density) (0.988 [0.977-0.999], p = 0.026) were cage-subsidence relevant according to univariate analysis. Low 1.25-mm EP-vBMD (0.992 [0.985, 0.999], p = 0.029) was an independent risk factor according to multifactorial analysis. Preoperative low EP-vBMD was an independent risk factor for postoperative cage subsidence after XLIF. EP-vBMD measured by most cortex-occupied ROI may be the optimal vBMD parameter for cage subsidence prediction.

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