ArticleScientific reports2025
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.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Diagnostic performance of the vertebral bone quality score for osteoporosis is minimally influenced by lipid parameters in lumbar fusion patients: a retrospective study.Asian spine journal · 2026Article
- NLR and PLR as Adjunctive Markers of MRI-Derived Vertebral Bone Quality in a Preoperative Lumbar Cohort.Therapeutics and clinical risk management · 2025Article
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Abstract
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 predicting SVF was 0.85, the cut-off was 64.40. In the HLP group, the AUC of VBQ was comparable with that of HU for SVF prediction, however, the sensitivity was lower in the HLP group (0.50 vs. 0.83). Furthermore, the AUC value of VBQ with HLP was lower than that of VBQ without HLP (0.78 vs. 0.90, P = 0.017). These findings demonstrated that both VBQ and HU can accurately predict the occurrence of SVF after PVA. HLP might cause a false increase of VBQ value, using HU could better assess bone quality and predict SVF occurrence when HLP is present.
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