Prediction of subsequent vertebral fracture after percutaneous vertebral augmentation using MRI-based vertebral bone quality and CT-based Hounsfield units: a retrospective cross-sectional study

Early human evidence such as case series or small samples is exploring possible benefits.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Subsequent vertebral fracture (SVF) is a common and refractory complication after percutaneous vertebral augmentation (PVA) for osteoporotic vertebral compression fracture (OVCF). Computed tomography (CT)-based Hounsfeld units (HU) and magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score can evaluate osteoporosis quantitatively, hyperlipidemia(HLP) might affect measurement result of VBQ score. The primary objective of this study is to compare the predictive capabilities of HU and VBQ for SVF, and to clarify the impact of hyperlipidemia on the predictive abilities. This study included consecutive 341 patients with OVCF who were treated with PVA from March 1, 2020, to December 31, 2022. A multivariate logistic regression analysis was used to determine the relationship between HU and VBQ and SVF. ROC curves were plotted to calculate area under curve (AUC), and hoc analysis and Youden index was used to determine cut-off values of HU and VBQ. Compared with the non-SVF group, VBQ (4.69 ± 0.35 vs. 4.14 ± 0.41, P < 0.001) was higher and HU (58.2 ± 13.81 vs. 81.2 ± 16.68, P < 0.001) was lower in the SVF group. On multivariate logistic regression analysis, higher VBQ (odds ratio (OR) = 23.47,P < 0.001) and lower HU (OR = 0.93,P < 0.001) are independent predictors for SVF. The AUC using VBQ for predicting SVF was 0.84, the cut-off was 4.28. The AUC using HU for predic

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

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