| Autor: |
Guobing Deng, Jichong Zhu, Qing Lu, Chong Liu, Tuo Liang, Jie Jiang, Hao Li, Chenxing Zhou, Shaofeng Wu, Tianyou Chen, Jiarui Chen, Yuanlin Yao, Shian Liao, Chaojie Yu, Shengsheng Huang, Xuhua Sun, Liyi Chen, Zhen Ye, Hao Guo, Wuhua Chen, Wenyong Jiang, Binguang Fan, Zhenwei Yang, Wenfei Gu, Yihan Wang, Xinli Zhan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
BMC Surgery, Vol 23, Iss 1, Pp 1-12 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2482 |
| DOI: |
10.1186/s12893-023-01959-y |
| Popis: |
Abstract Background In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. Results The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. Conclusion In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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