A systematic review of risk prediction models for venous thromboembolism patients after spinal surgery

doi:10.3761/j.issn.0254-1769.2026.20.016

Abstract

Abstract:

Objective To systematically evaluate risk prediction models for venous thromboembolism（VTE） after spinal surgery and provide evidence for clinical risk assessment and model optimization. Methods Chinese and English databases were systematically searched from inception to June 30，2026. The CHARMS checklist was used for literature screening and data extraction. Methodological quality was assessed using the PROBAST tool，and a meta-analysis was performed for models eligible for quantitative synthesis. Results A total of 15 studies were included，most of which were retrospective cohort studies. Logistic regression was the predominant modeling approach，while several studies incorporated the LASSO regression or machine learning algorithms. The main predictors included D-dimer，age，and surgery-related factors. The AUC ranged from 0.68 to 0.94，and most models demonstrated good calibration. External validation was performed in 6 studies，and 8 studies evaluated clinical utility using DCA. 4 studies were included in the meta-analysis，yielding a pooled AUC of 0.841（95%CI：0.783-0.899）. Conclusion Existing prediction models for postoperative VTE following spinal surgery generally demonstrate good predictive performance. However，their methodological quality remains suboptimal，and external validation is limited. The predictors remain focused on conventional VTE risk factors，with inadequate reflection of the specialized characteristics of spinal surgery. Future prospective multicenter studies are warranted to identify spinal-specific predictors and improve the generalizability and clinical applicability of these prediction models.

Key words: Spine, Venous Thromboembolism, Prediction Model, Systematic Review, Evidence-Based Nursing

摘要：

目的系统评价脊柱术后患者静脉血栓栓塞风险预测模型，为临床风险评估和模型优化提供循证依据。方法系统检索中英文数据库，检索时限为建库至2026年6月30日。采用预测模型系统评价数据提取清单筛选文献并提取数据，应用预测模型偏倚风险评估工具评价方法学质量，对可合并的模型进行Meta分析。结果共纳入15项研究，以回顾性队列研究为主。Logistic回归为主要建模方法，部分研究结合LASSO回归及机器学习算法。主要预测因子包括D-二聚体、年龄及手术相关因素。模型受试者操作特征曲线下面积为0.68~0.94，多数模型校准良好，6项研究进行了外部验证。8项研究采用决策曲线分析评价模型临床应用价值。Meta分析纳入4项研究，合并受试者操作特征曲线下面积为0.841（95%CI：0.783~0.899）。结论现有脊柱术后患者静脉血栓栓塞风险预测模型总体较好，但方法学质量欠佳、外部验证不足，预测因子仍以传统静脉血栓栓塞危险因素为主，对脊柱外科专科特征反映不足。未来应开展前瞻性多中心研究，筛选脊柱专科特异性预测因子，提升模型的泛化能力和临床应用价值。

关键词: 脊柱, 静脉血栓栓塞, 预测模型, 系统评价, 循证护理学

YU Qunfei, MA Yaojing, LIANG Xiaoying, YUE Lan, WANG Manman, YE Qun, YU Chengfei, SHENG Shaoying, REN Ying. A systematic review of risk prediction models for venous thromboembolism patients after spinal surgery[J]. Chinese Journal of Nursing, 2026, 61(20): 2847-2854.

余群飞, 马姚静, 梁晓英, 岳岚, 王曼曼, 叶群, 余城飞, 盛少英, 任英. 脊柱术后患者静脉血栓栓塞风险预测模型的系统评价[J]. 中华护理杂志, 2026, 61(20): 2847-2854.

Figures/Tables 2

References 30

[1]	McClendon J, Smith TR, O’Shaughnessy BA, et al. Time to event analysis for the development of venous thromboembolism after spinal fusion≥5 levels[J]. World Neurosurg, 2015, 84(3):826-833. DOI PMID
[2]	Yang SD, Liu H, Sun YP, et al. Prevalence and risk factors of deep vein thrombosis in patients after spine surgery:a retros-pective case-cohort study[J]. Sci Rep, 2015, 5:11834. DOI
[3]	Rockson HB, DiPaola CP, Connolly PJ, et al. Venous thromboem-bolism prophylaxis for patients having elective spine surgery:when,why,and how much[J]. J Bone Joint Surg Am, 2019, 101(13):1220-1229. DOI URL
[4]	鲁乔丹, 王磊, 邓海波, 等. 深静脉血栓形成患者健康相关生活质量评估工具的研究进展[J]. 中华护理杂志, 2022, 57(22):2791-2795. DOI
	Lu QD, Wang L, Deng HB, et al. Research progress of health-related quality of life assessment tools for patients with deep vein thrombosis[J]. Chin J Nurs, 2022, 57(22):2791-2795. DOI
[5]	Massaro AM, Frier S, Strot SM, et al. Revisiting anticoagulation in spine surgery:balancing venous thromboembolic events and epidural hematoma[J]. Global Spine J, 2025, 15(2):363-369. DOI URL
[6]	Cloney MB, Dhillon E, Hopkins B, et al. Chemoprophylactic an-ticoagulation after spine surgery does not increase epidural hematoma risk:a propensity-matched analysis[J]. Clin Spine Surg, 2024, 37(2):E97-E105.
[7]	Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical ap-praisal and data extraction for systematic reviews of predic-tion modelling studies:the CHARMS checklist[J]. PLoS Med, 2014, 11(10):e1001744. DOI URL
[8]	Moons KGM, Wolff RF, Riley RD, et al. PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med, 2019, 170(1):W1-W33. DOI URL
[9]	Wang KY, Ikwuezunma I, Puvanesarajah V, et al. Using predic-tive modeling and supervised machine learning to identify patients at risk for venous thromboembolism following posterior lumbar fusion[J]. Global Spine J, 2023, 13(4):1097-1103. DOI URL
[10]	Zhao YX, Kong XZ, Song KL, et al. Analysis of risk factors and establishment of prediction model for lower extremity deep vein thrombosis after lumbar fusion surgery[J]. BMC Surg, 2024, 24(1):392. DOI PMID
[11]	Wu XY, Wang Z, Zheng LL, et al. Construction and verifica-tion of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery[J]. Int J Med Inform, 2024, 192:105609. DOI URL
[12]	Kong WQ, Shao C, Du YK, et al. Nomogram for predicting venous thromboembolism after spinal surgery[J]. Eur Spine J, 2024, 33(3):1098-1108. DOI
[13]	Katiyar P, Chase H, Lenke LG, et al. Using machine learning(ML) models to predict risk of venous thromboembolism(VTE) following spine surgery[J]. Clin Spine Surg, 2023, 36(10):E453-E456.
[14]	Heo KY, Rajan PV, Khawaja S, et al. Machine learning approach to predict venous thromboembolism among patients undergo-ing multi-level spinal posterior instrumented fusion[J]. J Spine Surg, 2024, 10(2):214-223. DOI URL
[15]	He YT, Wang Z, Zheng X, et al. Construction of nomogram model for risk of venous thromboembolism after spine surgery based on thromboelastography and coagulation indices[J]. Front Med, 2024, 11:1486190. DOI URL
[16]	Gallagher DO, Bondar K, Hirase T, et al. Risk factors for and prediction of early thromboembolic disease following adult spinal deformity surgery:an analysis of >7,400 patients with spinal deformity[J]. J Bone Joint Surg Am, 2025, 107(11):1219-1226. DOI URL
[17]	高乾坤, 张文明, 曹向阳, 等. 腰椎间盘突出症患者发生下肢深静脉血栓的危险因素及列线图术前预测模型构建[J]. 中华实用诊断与治疗杂志, 2023, 37(9):896-902.
	Gao QK, Zhang WM, Cao XY, et al. Risk factors of lower extremity deep vein thrombosis in patients with lumbar disc herniation and construction of preoperative predictive nomo-gram model[J]. J Chin Pract Diagn Ther, 2023, 37(9):896-902.
[18]	陈浏阳. 脊柱手术术后静脉血栓形成的相关因素分析及预测模型构建[D]. 扬州: 扬州大学, 2025.
	Chen LY. Analysis of related factors of venous thrombosis after spinal surgery and construction of prediction model[D]. Yangzhou: Yangzhou University, 2025.
[19]	仇铁英, 高素园, 王卫星, 等. 脊柱外科患者术后静脉血栓危险因素分析及预测模型构建[J]. 中华现代护理杂志, 2022, 28(20):2694-2700.
	Qiu TY, Gao SY, Wang WX, et al. Risk factors for postope-rative venous thromboembolism in patients in department of spinal surgery and construction of a predictive model[J]. Chin J Mod Nurs, 2022, 28(20):2694-2700.
[20]	谷光辉. 后入路腰椎融合术后下肢深静脉血栓形成风险预测模型的构建[D]. 济南: 山东大学, 2022.
	Gu GH. Construction of a risk prediction model of deep vein thrombosis in patients after posterior lumbar fusion[D]. Jinan: Shandong University, 2022.
[21]	邵成. 脊柱术后并发静脉血栓栓塞症的危险因素分析及预测模型构建[D]. 青岛: 青岛大学, 2022.
	Shao C. Risk factors analysis and prediction model construc-tion of venous thromboembolism after spinal surgery[D]. Qing-dao: Qingdao University, 2022.
[22]	赵一祥. 脊柱外科术后下肢深静脉血栓形成的危险因素分析及预测模型建立[D]. 济南: 山东大学, 2023.
	Zhao YX. Risk factors of lower extremity deep venous throm-bosis after spinal surgery and development of a predictive model[D]. Jinan: Shandong University, 2023.
[23]	周国胜, 徐昆, 郭晓燕, 等. 基于围术期指标及CD63、α_2-AP、LPA建立的老年腰椎管狭窄症患者术后下肢深静脉血栓预警模型及验证[J]. 医学研究与战创伤救治, 2025, 38(6):614-619.
	Zhou GS, Xu K, Guo XY, et al. An early warning model and validation of postoperative lower extremity deep vein throm-bosis in elderly patients with lumbar spinal stenosis based on perioperative indicators and CD63,α2-AP,and LPA[J]. J Med Res Combat Trauma Care, 2025, 38(6):614-619.
[24]	Li X, Ma JL, Xue L, et al. Nomogram to assess the risk of deep venous thrombosis after posterior lumbar fusion:a retro-spective study[J]. Global Spine J, 2025, 15(4):2169-2175. DOI URL
[25]	Kelly M, Speciale A, Patton C, et al. A predictive model of preoperative risk factors for venous thromboembolism after fusion for degenerative conditions of the thoracolumbar spine[J]. J Orthop, 2020, 22:598-601. DOI URL
[26]	黄莹, 孙玛骥, 祝明秋, 等. 腰椎间盘突出症患者腰椎融合术后下肢深静脉血栓发生的风险预测模型[J]. 徐州医科大学学报, 2025, 45(12):905-910.
	Huang Y, Sun MJ, Zhu MQ, et al. Risk prediction model for deep venous thrombosis in the lower limbs after lumbar interbody fusion in patients with lumbar disc herniation[J]. Acta Acad Med Xuzhou, 2025, 45(12):905-910.
[27]	Fu GX, Wang Y, Tan DW, et al. Development and validation of a nomogram for predicting postoperative lower extremity deep vein thrombosis in patients with traumatic spinal fractures:a retrospective study[J]. Peer J, 2026, 14:e21184. DOI URL
[28]	范志英, 焦文仓, 王亚红, 等. 间歇充气加压装置不同使用时长对预防腰椎融合术中患者静脉血栓的效果评价[J]. 中华护理杂志, 2022, 57(4):449-454. DOI
	Fan ZY, Jiao WC, Wang YH, et al. Intervention study of app-lying intermittent pneumatic compression devices with diffe-rent duration in lumbar fusion surgery[J]. Chin J Nurs, 2022, 57(4):449-454.
[29]	Afshari A, Ageno W, Ahmed A, et al. European guidelines on perioperative venous thromboembolism prophylaxis:executive summary[J]. Eur J Anaesthesiol, 2018, 35(2):77-83. DOI PMID
[30]	米元元, 陈婷, 周英凤, 等. 脊柱外科术后患者深静脉血栓预防及管理的最佳证据总结[J]. 中华护理杂志, 2019, 54(2):288-294. DOI
	Mi YY, Chen T, Zhou YF, et al. Evidence summary for preven-tion and management of deep venous thrombosis after spinal surgery[J]. Chin J Nurs, 2019, 54(2):288-294.