PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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：
■脊柱手术中静脉血栓栓塞（VTE）的预防缺乏共识强调了识别有风险患者的重要性。这项研究结合了机器学习（ML）模型来评估接受后路脊柱器械融合的患者VTE的关键危险因素。
■数据来自IBMMarketScan数据库[2009-2021]，用于≥18岁的患者接受了脊柱后路器械（3-6级），排除创伤，恶性肿瘤,和感染。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logistic回归在内的几种ML模型，对VTE的危险因素进行了调查和比较。线性支持向量机(LSVM)，随机森林,XGBoost,和神经网络。
■在接受后路器械（3-6级）脊柱融合术的141,697名患者中，总体90天VTE率为3.81%.LSVM模型证明了曲线下面积(AUC)为0.68的最佳预测。VTE预测的最重要特征包括VTE的远程历史，诊断为慢性高凝，转移性癌症，偏瘫,和慢性肾脏疾病。没有这五个关键危险因素的患者90天VTE率为2.95%。具有越来越多关键危险因素的患者术后发生VTE的风险更高。
■使用不同ML模型对数据的分析确定了与VTE最密切相关的5个关键变量。使用这些变量，我们开发了一个简单的风险模型,在后路脊柱融合术后90天内,其加性比值比范围为2.80(1个风险因素)至46.92(4个风险因素).这些发现可以帮助外科医生对患者的VTE风险进行风险分层，并可能指导后续的化学预防。
■数据来自IBMMarketScan数据库[2009-2021]，用于≥18岁的患者接受了脊柱后路器械（3-6级），排除创伤，恶性肿瘤,和感染。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logistic回归在内的几种ML模型，对VTE的危险因素进行了调查和比较。线性支持向量机(LSVM)，随机森林,XGBoost,和神经网络。
■在接受后路器械（3-6级）脊柱融合术的141,697名患者中，总体90天VTE率为3.81%.LSVM模型证明了曲线下面积(AUC)为0.68的最佳预测。VTE预测的最重要特征包括VTE的远程历史，诊断为慢性高凝，转移性癌症，偏瘫,和慢性肾脏疾病。没有这五个关键危险因素的患者90天VTE率为2.95%。具有越来越多关键危险因素的患者术后发生VTE的风险更高。
■使用不同ML模型对数据的分析确定了与VTE最密切相关的5个关键变量。使用这些变量，我们开发了一个简单的风险模型,在后路脊柱融合术后90天内,其加性比值比范围为2.80(1个风险因素)至46.92(4个风险因素).这些发现可以帮助外科医生对患者的VTE风险进行风险分层，并可能指导后续的化学预防。
