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预测接受多级脊柱后路器械融合术患者静脉血栓栓塞的机器学习方法。

Machine learning approach to predict venous thromboembolism among patients undergoing multi-level spinal posterior instrumented fusion.

作者信息

Heo Kevin Y, Rajan Prashant V, Khawaja Sameer, Barber Lauren A, Yoon Sangwook Tim

机构信息

Department of Orthopaedic Surgery, Emory University School of Medicine, Atlanta, GA, USA.

出版信息

J Spine Surg. 2024 Jun 21;10(2):214-223. doi: 10.21037/jss-24-8. Epub 2024 Jun 17.

Abstract

BACKGROUND

The absence of consensus for prophylaxis of venous thromboembolism (VTE) in spine surgery underscores the importance of identifying patients at risk. This study incorporated machine learning (ML) models to assess key risk factors of VTE in patients who underwent posterior spinal instrumented fusion.

METHODS

Data was collected from the IBM MarketScan Database [2009-2021] for patients ≥18 years old who underwent spinal posterior instrumentation (3-6 levels), excluding traumas, malignancies, and infections. VTE incidence (deep vein thrombosis and pulmonary embolism) was recorded 90-day post-surgery. Risk factors for VTE were investigated and compared through several ML models including logistic regression, linear support vector machine (LSVM), random forest, XGBoost, and neural networks.

RESULTS

Among the 141,697 patients who underwent spinal fusion with posterior instrumentation (3-6 levels), the overall 90-day VTE rate was 3.81%. The LSVM model demonstrated the best prediction with an area under the curve (AUC) of 0.68. The most important features for prediction of VTE included remote history of VTE, diagnosis of chronic hypercoagulability, metastatic cancer, hemiplegia, and chronic renal disease. Patients who did not have these five key risk factors had a 90-day VTE rate of 2.95%. Patients who had an increasing number of key risk factors had subsequently higher risks of postoperative VTE.

CONCLUSIONS

The analysis of the data with different ML models identified 5 key variables that are most closely associated with VTE. Using these variables, we have developed a simple risk model with additive odds ratio ranging from 2.80 (1 risk factor) to 46.92 (4 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for VTE risk, and potentially guide subsequent chemoprophylaxis.

摘要

背景

脊柱手术中静脉血栓栓塞症(VTE)预防措施缺乏共识,凸显了识别高危患者的重要性。本研究纳入机器学习(ML)模型,以评估接受后路脊柱内固定融合术患者发生VTE的关键风险因素。

方法

从IBM MarketScan数据库[2009 - 2021年]收集数据,纳入年龄≥18岁、接受脊柱后路内固定术(3 - 6节段)的患者,排除创伤、恶性肿瘤和感染患者。术后90天记录VTE发生率(深静脉血栓形成和肺栓塞)。通过包括逻辑回归、线性支持向量机(LSVM)、随机森林、XGBoost和神经网络在内的几种ML模型,对VTE的风险因素进行研究和比较。

结果

在141,697例接受后路脊柱内固定融合术(3 - 6节段)的患者中,总体90天VTE发生率为3.81%。LSVM模型显示出最佳预测效果,曲线下面积(AUC)为0.68。预测VTE的最重要特征包括VTE既往史、慢性高凝状态诊断、转移性癌症、偏瘫和慢性肾病。没有这五个关键风险因素的患者90天VTE发生率为2.95%。关键风险因素数量增加的患者术后发生VTE的风险更高。

结论

使用不同的ML模型对数据进行分析,确定了与VTE最密切相关的5个关键变量。利用这些变量,我们开发了一个简单的风险模型,后路脊柱融合术后90天内的相加比值比范围为2.80(1个风险因素)至46.92(4个风险因素)。这些发现有助于外科医生对患者进行VTE风险分层,并可能指导后续的化学预防。

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