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. 2022 May 3:13:21514593221098620.
doi: 10.1177/21514593221098620. eCollection 2022.

Establishment and Validation of a Nomogram for the Risk of New Vertebral Compression Fractures After Percutaneous Vertebroplasty in Patients With Osteoporotic Vertebral Compression Fractures: A Retrospective Study

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Establishment and Validation of a Nomogram for the Risk of New Vertebral Compression Fractures After Percutaneous Vertebroplasty in Patients With Osteoporotic Vertebral Compression Fractures: A Retrospective Study

FuCheng Bian et al. Geriatr Orthop Surg Rehabil. .

Abstract

Purpose: New vertebral compression fractures(NVCFs) after minimally invasive surgery in patients with osteoporotic vertebral compression fracture (OVCF) is a challenging issue worldwide. Predicting the occurrence of NVCFs is key to addressing such questions. Therefore, we aimed to investigate the risk factors for patients who developed NVCFs after undergoing surgical treatment and establish a nomogram model to reduce the occurrence of NVCFs.

Methods: This study is a retrospective analysis that collected the general characteristics and surgical features of patients who underwent surgical treatment at 2 central institutions between January 2017 and December 2020. Patients were divided into training and testing sets based on the presence or absence of NVCFs. Independent risk factors for NVCFs were obtained in the training set of patients, and then a nomogram model was constructed. Internal and external validation of the nomogram model was performed using the consistency index (C index), receiver operating characteristic curve(ROC), calibration curves, and decision curve analysis (DCA).

Results: A total of 562 patients were included in this study. Patients from the first center were used for nomogram construction and internal validation, and patients from the second center were used as an external validation population. Multivariate regression analysis showed that age, Hounsfield unit (Hu) value, cement leakage, and thoracolumbar (TL) junction fracture were independent risk factors for NVCFs after minimally invasive surgery. The C index was .85, and the validation of internal and external validation shows that the predicted values of the established model is in good agreement with the actual values.

Conclusions: In this study, 4 independent risk factors were obtained by regression analysis, and a nomogram model was constructed to guide clinical work. The application of this model can help surgeons to make more accurate judgments to prevent the occurrence of NVCFs.

Keywords: new vertebral compression fracture; nomogram; osteoporotic vertebral compression fracture; risk factor.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
CT values were measured by PACS. (A) sagittal image of the lumbar spine with 3 tangents made on the measured vertebrae, corresponding to the 3 levels of (B), (C) and (D) in the axial position. The largest elliptical region of interest(ROI) containing only bone trabeculae was drawn in the axial position to obtain the average Hu value.
Figure 2.
Figure 2.
The nomogram for predicting New vertebral compression fractures in patients with osteoporotic vertebral compression fracture after percutaneous kyphoplasty operation. Each risk factor was assigned a point, which was summed to give a total point that corresponded to the probability of the hazard on the bottom row of the figure according to the total point.
Figure 3.
Figure 3.
Comparison of the area under the receiver operating characteristic curve between nomogram independent predictors in the training set (A), testing set (B), and validation population(C)
Figure 4.
Figure 4.
Comparison of calibration curves between the training set (A), testing set (B), and validation population(C).
Figure 5.
Figure 5.
Comparison of decision curve analyses between the training set (A), testing set (B), and validation population(C).

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