创伤性胸腰椎骨折术前深静脉血栓形成预测模型的建立与验证-动脉网

Development and validation of a predictive model for preoperative deep vein thrombosis following traumatic thoracolumbar fractures

Nature 等信源发布 2024-08-22 02:23

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AbstractAlthough a sequential work-up for deep vein thrombosis has reached agreement worldwide, the mysterious nature of DVT following fractures brings challenges to early diagnosis and intervention. The objective of the present study was to develop and validate a nomogram for predicting preoperative DVT risk in patients with thoracolumbar fractures using readily available clinical data.

摘要虽然深静脉血栓形成的顺序检查已在世界范围内达成共识，但骨折后DVT的神秘性质给早期诊断和干预带来了挑战。本研究的目的是使用现成的临床数据开发和验证用于预测胸腰椎骨折患者术前DVT风险的列线图。

Of the 1350 patients, 930 were randomly assigned to the training cohort. A prediction model was established and visualized as a nomogram based on eight predictors related to preoperative DVT. The performance of the model was tested by the receiver operating characteristic curve, Hosmer–Lemeshow test, calibration curve, and decision curve analysis.

。建立预测模型并将其可视化为基于与术前DVT相关的八个预测因子的列线图。通过接收器工作特性曲线，Hosmer-Lemeshow测试，校准曲线和决策曲线分析来测试模型的性能。

We further verified the model in the validation cohort. The AUCs of the prediction model were 0.876 and 0.853 in training and validation cohorts, respectively. The Hosmer–Lemeshow test demonstrated good fitness in the training set (X2 = 5.913, P = 0.749) and the validation set (X2 = 9.460, P = 0.396).

我们在验证队列中进一步验证了该模型。预测模型的AUC在训练和验证队列中分别为0.876和0.853。Hosmer-Lemeshow测试显示训练集（X2=5.913，P=0.749）和验证集（X2=9.460，P=0.396）具有良好的适应性。

Calibration and decision curve analyses performed well in training and validation sets. In short, we developed a prediction model for preoperative DVT risk in patients with thoracolumbar fractures and verified its accuracy and clinical utility..

校准和决策曲线分析在训练和验证集中表现良好。简而言之，我们开发了胸腰椎骨折患者术前DVT风险的预测模型，并验证了其准确性和临床实用性。。

IntroductionDeep vein thrombosis (DVT) and pulmonary embolism (PE), pivotal concerns in the domain of inpatient morbidity and mortality, present a pronounced challenge in the context of traumatic injuries. Among the pantheon of traumatic afflictions, thoracolumbar fractures stand out due to their prevalence and the consequent risk of DVT, which has been documented to afflict between 4.3 and 14.5% of patients in the preoperative phase, notwithstanding the implementation of prophylactic measures1,2.

引言深静脉血栓形成（DVT）和肺栓塞（PE）是住院患者发病率和死亡率领域的关键问题，在创伤性损伤的背景下提出了明显的挑战。在创伤痛苦的万神殿中，胸腰椎骨折由于其患病率和随之而来的DVT风险而脱颖而出，尽管实施了预防措施，但已记录到术前阶段有4.3%至14.5%的患者患有DVT[1,2]。

The extant guidelines advocate for a multifaceted diagnostic approach entailing the assessment of clinical pretest probability, D-dimer assays, and imaging examinations3. This scheme can exclude DVT in about one-third of ambulatory outpatients in both the primary and hospital care setting4,5. Nevertheless, this paradigm exhibits diminished efficacy in individuals with an elevated predisposition to DVT or those with inherently high D-dimer levels, encompassing, but not limited to, elderly patients, pregnant women, and subjects undergoing treatment for major traumas or fractures6,7.The literature reveals a plethora of DVT risk assessment models deployed in clinical settings; however, subsequent investigations frequently cast doubt on their predictive accuracy, particularly in hospitalized cohorts.

现有的指南提倡采用多方面的诊断方法，包括评估临床预测试概率，D-二聚体测定和影像学检查3。该方案可以排除初级和医院护理环境中约三分之一门诊患者的DVT[4,5]。；然而，随后的调查经常对其预测准确性产生怀疑，特别是在住院人群中。

The Caprini risk assessment model8, despite its comprehensive nature, necessitates labor-intensive and costly laboratory evaluations (such as positive Factor V Leiden, prothrombin G20210A mutation, and elevated lupus anticoagulant) that may not be universally accessible, thereby potentially undermining the model's evaluative capacity.

Caprini风险评估模型8尽管具有全面的性质，但仍需要进行劳动密集型和昂贵的实验室评估（例如阳性因子V莱顿，凝血酶原G20210A突变和狼疮抗凝剂升高），这些评估可能无法普遍获得，从而潜在地破坏了模型的评估能力。

Moreover, its complexity could preclude rapid application in clinical scenarios. The Wells score, while established for the outpatient demographic, engen.

此外，其复杂性可能会妨碍在临床场景中的快速应用。Wells评分是为门诊人口统计而建立的，engen。

Data availability

数据可用性

All the data used during the current study are available from the corresponding author on reasonable request.

本研究中使用的所有数据均可根据合理要求从通讯作者处获得。

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Liu, B., Pan, J., Zong, H. & Wang, Z. The risk factors and predictive nomogram of human albumin infusion during the perioperative period of posterior lumbar interbody fusion: A study based on 2015–2020 data from a local hospital. J. Orthop. Surg. Res. 16(1), 654 (2021).Article

Liu，B.，Pan，J.，Zong，H。＆Wang，Z。后路腰椎椎体间融合术围手术期人类白蛋白输注的危险因素和预测列线图：基于当地医院2015-2020年数据的研究。J、骨科。Surg.Res.16（1），654（2021）。文章

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Wang, H. et al. Development and validation of a nomogram for prediction of the risk of positive hidden blood loss in the perioperative period of single-level thoracolumbar burst fracture. J. Orthop. surg. Res. 16(1), 560 (2021).Article

。J、骨科。surg.Res.16（1），560（2021）。文章

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Download referencesAcknowledgementsThe Key Laboratory of Biomechanics of Hebei Province provided the site for querying data.FundingThe research was supported by the Innovation Project for Graduate Students of Hebei Medical University (XCXZZS202323), Medical Scientific Research Project of Hebei Provincial Health Commission (20221170), and Clinical Nursing Research Funding 2023 (ydsy202301).

下载参考文献致谢河北省生物力学重点实验室提供了查询数据的网站。资助该研究得到了河北医科大学研究生创新项目（XCXZZS202323），河北省卫生委员会医学科研项目（20221170）和临床护理研究基金2023（ydsy202301）的支持。

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Author informationAuthors and AffiliationsDepartment of Orthopaedic Surgery, Hebei Medical University Third Hospital, Shijiazhuang, 050051, Hebei, People’s Republic of ChinaJiangtao Ma, Miao Tian, Yanbin Zhu, Jinglve Hu, Yingze Zhang & Xiuting LiOrthopaedic Institution of Hebei Province, Shijiazhuang, 050051, Hebei, People’s Republic of ChinaYanbin Zhu, Jinglve Hu & Yingze ZhangHebei Orthopaedic Clinical Research Center, Hebei Medical University Third Hospital, Shijiazhuang, 050051, Hebei, People’s Republic of ChinaJinglve Hu & Yingze ZhangKey Laboratory of Biomechanics of Hebei Province, Shijiazhuang, 050051, Hebei, People’s Republic of ChinaYingze ZhangChinese Academy of Engineering, Beijing, 100088, People’s Republic of ChinaYingze ZhangNHC Key Laboratory of Intelligent Orthopaedic Equipment, Hebei Medical University Third Hospital, Shijiazhuang, 050051, Hebei, People’s Republic of ChinaYingze ZhangAuthorsJiangtao MaView author publicationsYou can also search for this author in.

资助者在研究设计，数据收集和分析，决定发表或准备稿件方面没有任何作用。作者信息作者和附属机构河北医科大学第三医院骨科，石家庄，050051中华人民共和国北京中国工程院张迎泽100088河北医科大学第三医院智能骨科设备重点实验室，河北石家庄050051，中华人民共和国张迎泽作者蒋涛MaView作者出版物您也可以在中搜索该作者。

PubMed Google ScholarMiao TianView author publicationsYou can also search for this author in

PubMed Google Scholarmaio TianView作者出版物您也可以在

PubMed Google ScholarYanbin ZhuView author publicationsYou can also search for this author in

PubMed Google ScholarYanbin ZhuView作者出版物您也可以在

PubMed Google ScholarJinglve HuView author publicationsYou can also search for this author in

PubMed Google ScholarJinglve HuView作者出版物您也可以在

PubMed Google ScholarYingze ZhangView author publicationsYou can also search for this author in

PubMed Google ScholarYingze ZhangView作者出版物您也可以在

PubMed Google ScholarXiuting LiView author publicationsYou can also search for this author in

PubMed Google ScholarXiuting LiView作者出版物您也可以在

PubMed Google ScholarContributionsY.Z.Z. and X.T.L. designed the study. M.T. and J.L.H. searched relevant studies and collected data on variables of interest. J.T.M. and Y.B.Z. analyzed and interpreted the data. J.T.M. wrote the manuscript. The authors read and approved the final manuscript.Corresponding authorsCorrespondence to.

PubMed谷歌学术贡献。Z、 Z.和X.T.L.设计了这项研究。M、 T.和J.L.H.搜索了相关研究并收集了有关感兴趣变量的数据。J、 T.M.和Y.B.Z.分析并解释了数据。J、 T.M.写了手稿。作者阅读并批准了最终稿件。通讯作者通讯。

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Reprints and permissionsAbout this articleCite this articleMa, J., Tian, M., Zhu, Y. et al. Development and validation of a predictive model for preoperative deep vein thrombosis following traumatic thoracolumbar fractures.

转载和许可本文引用本文Ma，J.，Tian，M.，Zhu，Y。等人。创伤性胸腰椎骨折术前深静脉血栓形成预测模型的开发和验证。

Sci Rep 14, 19547 (2024). https://doi.org/10.1038/s41598-024-70464-wDownload citationReceived: 11 June 2023Accepted: 16 August 2024Published: 22 August 2024DOI: https://doi.org/10.1038/s41598-024-70464-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1419547（2024）。https://doi.org/10.1038/s41598-024-70464-wDownload引文收到日期：2023年6月11日接受日期：2024年8月16日发布日期：2024年8月22日OI：https://doi.org/10.1038/s41598-024-70464-wShare本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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