Magnetic resonance imaging-based bone and muscle quality parameters for predicting clinical subsequent vertebral fractures after percutaneous vertebral augmentation.

Abstract

Background: Clinical subsequent vertebral fracture (SVF) is a common complication following percutaneous vertebral augmentation treatment for osteoporotic vertebral compression fracture. Magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score, cross-sectional area (CSA), and degree of fat infiltration (DFI) of paravertebral muscles are effective predictors of spinal surgery-related complications. However, the relationship between these parameters and SVF remains unclear. The purpose of this study was to evaluate the utility of these MRI-based bone and muscle quality parameters for predicting SVF after percutaneous vertebral augmentation.

Methods: This retrospective study included consecutive patients with osteoporotic vertebral compression fracture treated with percutaneous vertebral augmentation at Xuzhou Central Hospital between January 2017 and December 2020. Clinical SVF was diagnosed if there was new episode of back pain and a confirmed acute fracture on MRI. Noncontrast T1-weighted MRI and axial T2-weighted MRI were used to determine the VBQ score and measure CSA and DFI, respectively. A multivariable logistic regression analysis adjusted for confounding factors was performed to determine the correlation between VBQ score, DFI, CSA, and SVF. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive ability of SVF. The DeLong test was used to compare the predictive ability. Pearson correlation analysis was used to characterize the relationships between VBQ score and both CSA and DFI.

Results: A total of 289 patients were included in this study, and 41 (14.2%) patients developed SVF. Compared with the non-SVF group, the SVF group had a higher VBQ score (3.83 vs. 3.28; P<0.05) and DFI (66.3% vs. 44.1%; P<0.001). The multivariable regression analysis revealed that a higher VBQ score [odds ratio (OR) =3.66; P<0.001] and DFI (OR =3.72; P<0.001) were associated with SVF. The AUC of the VBQ score was 0.863 (cutoff =3.49). Similarly, the AUC of DFI was 0.851 (cutoff =48.2%). The AUC for the combination of VBQ score and DFI in predicting SVF was 0.925 (P<0.001). According to the Delong test, the AUC of the combined model was higher than that of the VBQ score alone (0.925 vs. 0.863; P=0.0389) and DFI alone (0.925 vs. 0.851; P=0.0254). The Pearson correlation showed that the VBQ score was positively correlated with DFI (r=0.647; P<0.001) while no significant correlation was present between the VBQ score and CSA (r=-0.039; P=0.7495).

Conclusions: The VBQ score and DFI were independent predictors for clinical SVF after percutaneous vertebral augmentation. The combination of VBQ score and DFI significantly improved the predictive accuracy. Moreover, there was a significant positive correlation between the VBQ score and DFI.