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The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty

PURPOSE: This study aims to develop an algorithm to predict cement injection volumes in patients with spine metastases treated with percutaneous vertebroplasty (PVP). Risk factors were also analyzed for intra-spinal canal cement leakages. PATIENTS AND METHODS: A retrospective analysis of 584 vertebr...

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Autores principales: Cui, Yunpeng, Pan, Yuanxing, Lei, Mingxing, Mi, Chuan, Wang, Bing, Shi, Xuedong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234973/
https://www.ncbi.nlm.nih.gov/pubmed/32523346
http://dx.doi.org/10.2147/TCRM.S253046
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author Cui, Yunpeng
Pan, Yuanxing
Lei, Mingxing
Mi, Chuan
Wang, Bing
Shi, Xuedong
author_facet Cui, Yunpeng
Pan, Yuanxing
Lei, Mingxing
Mi, Chuan
Wang, Bing
Shi, Xuedong
author_sort Cui, Yunpeng
collection PubMed
description PURPOSE: This study aims to develop an algorithm to predict cement injection volumes in patients with spine metastases treated with percutaneous vertebroplasty (PVP). Risk factors were also analyzed for intra-spinal canal cement leakages. PATIENTS AND METHODS: A retrospective analysis of 584 vertebrae in 251 patients. Patients and vertebrae were divided into three groups based on grades of tumor invasion to the spinal cord. Patients with the complete posterior wall of vertebrae were classified into group A, patients without the complete posterior wall of vertebrae but with normal Dural sac were classified into group B, and patients with deformation of the Dural sac but without neurological symptom were classified into group C. We systematically reviewed demographic data, clinical parameters, radiology features, and cement leakages among the three groups. The multiple linear regressions were used to screen potential risk factors and develop the algorithm to predict injected cement volumes in vertebrae. Significant factors were included in the algorithm. Potential risk factors for intra-spinal canal cement leakage were analyzed using the multiple logistic regressions. RESULTS: In the study, 17.1% (100/584) of vertebrae occurred cement leakages. Vertebrae in group C (28.6%, 8/28) had the highest cement leakage rate than patients in group A (14.4%, 61/424) and B (23.5%, 31/132) (P=0.014). Vertebrae in group C (14.3%, 4/28) were also more prone to intra-spinal canal leakages (P=0.003). The multiple logistic analysis showed that the Bilsky scale was significantly associated with intra-spinal canal cement leakages (P<0.001). The multiple linear regression analysis showed that intercept (P<0.001), treated vertebrae level (P<0.001), cortical osteolytic destruction in posterior wall (P<0.001), and Bilsky scale (P=0.014) were significant and those variables were included in the algorithm. The algorithm was Y=3.1627-0.8677×treated vertebrae level-0.6182×cortical osteolytic destruction in the posterior wall-0.2819×Bilsky scale. CONCLUSION: An algorithm is proposed and can be used to calculate cement injection volumes in spine metastases treated with PVP. This algorithm can facilitate surgical planning and guide cement injections. Bilsky scale is an independent risk factor for intra-spinal canal cement leakages. We do not recommend PVP treated in patients with a Bilsky scale of 2 and 3 mainly due to a high rate of intra-spinal canal cement leakages.
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spelling pubmed-72349732020-06-09 The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty Cui, Yunpeng Pan, Yuanxing Lei, Mingxing Mi, Chuan Wang, Bing Shi, Xuedong Ther Clin Risk Manag Original Research PURPOSE: This study aims to develop an algorithm to predict cement injection volumes in patients with spine metastases treated with percutaneous vertebroplasty (PVP). Risk factors were also analyzed for intra-spinal canal cement leakages. PATIENTS AND METHODS: A retrospective analysis of 584 vertebrae in 251 patients. Patients and vertebrae were divided into three groups based on grades of tumor invasion to the spinal cord. Patients with the complete posterior wall of vertebrae were classified into group A, patients without the complete posterior wall of vertebrae but with normal Dural sac were classified into group B, and patients with deformation of the Dural sac but without neurological symptom were classified into group C. We systematically reviewed demographic data, clinical parameters, radiology features, and cement leakages among the three groups. The multiple linear regressions were used to screen potential risk factors and develop the algorithm to predict injected cement volumes in vertebrae. Significant factors were included in the algorithm. Potential risk factors for intra-spinal canal cement leakage were analyzed using the multiple logistic regressions. RESULTS: In the study, 17.1% (100/584) of vertebrae occurred cement leakages. Vertebrae in group C (28.6%, 8/28) had the highest cement leakage rate than patients in group A (14.4%, 61/424) and B (23.5%, 31/132) (P=0.014). Vertebrae in group C (14.3%, 4/28) were also more prone to intra-spinal canal leakages (P=0.003). The multiple logistic analysis showed that the Bilsky scale was significantly associated with intra-spinal canal cement leakages (P<0.001). The multiple linear regression analysis showed that intercept (P<0.001), treated vertebrae level (P<0.001), cortical osteolytic destruction in posterior wall (P<0.001), and Bilsky scale (P=0.014) were significant and those variables were included in the algorithm. The algorithm was Y=3.1627-0.8677×treated vertebrae level-0.6182×cortical osteolytic destruction in the posterior wall-0.2819×Bilsky scale. CONCLUSION: An algorithm is proposed and can be used to calculate cement injection volumes in spine metastases treated with PVP. This algorithm can facilitate surgical planning and guide cement injections. Bilsky scale is an independent risk factor for intra-spinal canal cement leakages. We do not recommend PVP treated in patients with a Bilsky scale of 2 and 3 mainly due to a high rate of intra-spinal canal cement leakages. Dove 2020-05-14 /pmc/articles/PMC7234973/ /pubmed/32523346 http://dx.doi.org/10.2147/TCRM.S253046 Text en © 2020 Cui et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Cui, Yunpeng
Pan, Yuanxing
Lei, Mingxing
Mi, Chuan
Wang, Bing
Shi, Xuedong
The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title_full The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title_fullStr The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title_full_unstemmed The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title_short The First Algorithm Calculating Cement Injection Volumes in Patients with Spine Metastases Treated with Percutaneous Vertebroplasty
title_sort first algorithm calculating cement injection volumes in patients with spine metastases treated with percutaneous vertebroplasty
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234973/
https://www.ncbi.nlm.nih.gov/pubmed/32523346
http://dx.doi.org/10.2147/TCRM.S253046
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