Using machine learning in the prediction of symptomatic venous thromboembolism following ankle fracture

IF 1.9 3区 医学 Q2 ORTHOPEDICS
Nour Nassour , Bardiya Akhbari , Noopur Ranganathan , David Shin , Hamid Ghaednia , Soheil Ashkani-Esfahani , Christopher W. DiGiovanni , Daniel Guss
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引用次数: 0

Abstract

Background

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in the trauma setting, and both prediction and prevention of VTE have long been a concern for healthcare providers in orthopedic surgery. The purpose of this study was to evaluate the use of novel statistical analysis and machine-learning in predicting the risk of VTE and the usefulness of prophylaxis following ankle fractures.

Methods

The medical profiles of 16,421 patients with ankle fractures were screened retrospectively for symptomatic VTE. In total, 238 patients sustaining either surgical or nonsurgical treatment for ankle fracture with subsequently confirmed VTE within 180 days following the injury were placed in the case group. Alternatively, 937 patients who sustained ankle fractures managed similarly but had no documented evidence of VTE were randomly chosen as the control group. Individuals from both the case and control populations were also divided into those who had received VTE prophylaxis and those who had not. Over 110 variables were included. Conventional statistics and machine learning methods were used for data analysis.

Results

Patients who had a motor vehicle accident, surgical treatment, increased hospital stay, and were on warfarin were shown to have a higher incidence of VTE, whereas patients who were on statins had a lower incidence of VTE. The highest Area Under the Receiver Operating Characteristic Curves (AUROC) showing the performance of our machine learning approach was 0.88 with 0.94 sensitivity and 0.36 specificity. The most balanced performance was seen in a model that was trained using selected variables with 0.86 AUROC, 0.75 sensitivity, and 0.85 specificity.

Conclusion

By using machine learning, this study successfully pinpointed several predictive factors linked to the occurrence or absence of VTE in patients who experienced an ankle fracture. Training these algorithms using larger, more granular, and multicentric data will further increase their validity and reliability and should be considered the standard for the development of such algorithms.

Level of evidence

Case-Control study – 3

利用机器学习预测踝关节骨折后无症状静脉血栓栓塞症
背景静脉血栓栓塞症(VTE)是创伤环境中发病率和死亡率的主要原因,VTE 的预测和预防一直是骨科手术医护人员关注的问题。本研究旨在评估新型统计分析和机器学习在预测踝关节骨折后 VTE 风险和预防措施中的应用。总共有 238 名踝关节骨折患者接受了手术或非手术治疗,并在受伤后 180 天内证实患有 VTE,这些患者被归入病例组。另外,还随机选择了 937 名踝关节骨折患者作为对照组,这些患者接受了类似的治疗,但没有 VTE 的记录证据。病例组和对照组的患者也被分为接受过 VTE 预防治疗和未接受过 VTE 预防治疗的两组。其中包括 110 多个变量。结果显示,发生过机动车事故、接受过手术治疗、住院时间延长以及服用华法林的患者VTE发生率较高,而服用他汀类药物的患者VTE发生率较低。显示我们机器学习方法性能的最高接收者工作特征曲线下面积(AUROC)为 0.88,灵敏度为 0.94,特异度为 0.36。通过使用机器学习,本研究成功地找出了与踝关节骨折患者是否发生 VTE 相关的几个预测因素。使用更大、更精细和多中心的数据来训练这些算法将进一步提高其有效性和可靠性,并应被视为开发此类算法的标准。
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来源期刊
Foot and Ankle Surgery
Foot and Ankle Surgery ORTHOPEDICS-
CiteScore
4.60
自引率
16.00%
发文量
202
期刊介绍: Foot and Ankle Surgery is essential reading for everyone interested in the foot and ankle and its disorders. The approach is broad and includes all aspects of the subject from basic science to clinical management. Problems of both children and adults are included, as is trauma and chronic disease. Foot and Ankle Surgery is the official journal of European Foot and Ankle Society. The aims of this journal are to promote the art and science of ankle and foot surgery, to publish peer-reviewed research articles, to provide regular reviews by acknowledged experts on common problems, and to provide a forum for discussion with letters to the Editors. Reviews of books are also published. Papers are invited for possible publication in Foot and Ankle Surgery on the understanding that the material has not been published elsewhere or accepted for publication in another journal and does not infringe prior copyright.
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