机器学习方法用于预测静脉血栓栓塞在接受多级脊柱后部仪器融合的患者中的静脉血栓塞
Kevin Y Heo1, Prashant V Rajan1, Sameer Khawaja1
1Department of Orthopaedic Surgery, Emory University School of Medicine, Atlanta, GA, USA.
Journal of spine surgery (Hong Kong)
|July 8, 2024
概括
机器学习确定了脊椎手术后静脉血栓栓塞 (VTE) 的关键风险因素. 一个简单的风险模型可以帮助外科医生识别高风险患者进行VTE预防.
科学领域:
- 脊柱外科手术 脊柱外科手术
- 血管外科 血管外科
- 机器学习在医学中的应用
背景情况:
- 静脉血栓塞栓症 (VTE) 预防在脊椎手术中缺乏共识.
- 识别患有VTE风险的患者至关重要.
研究的目的:
- 利用机器学习模型,在接受后脊椎仪器融合的患者中识别VTE的关键风险因素.
- 在这个患者群体中开发VTE风险分层模型.
主要方法:
- 对IBM MarketScan数据库 (2009-2021) 的回顾性分析.
- 包括接受后脊柱仪器 (3-6级) 的患者 (≥18岁),不包括创伤,恶性瘤和感染.
- 应用机器学习模型 (逻辑回归,LSVM,随机森林,XGBoost,神经网络) 来预测90天VTE发病率 (深静脉血栓和肺栓塞).
主要成果:
- 在141,697名患者中,90天VTE的总比率为3.81%.
- 线性支向量机 (LSVM) 模型实现了最高的预测性能 (AUC 0.68).
- 静脉瘤的关键预测因素包括远程静脉瘤史,慢性高凝性,转移性癌症,半,慢性病.
结论:
- 确定了五个关键变量与VTE风险密切相关.
- 一个简单的风险模型表明,随着风险因素数量增加 (2.80至46.92),VTE的几率比率增加.
- 这个模型可以帮助外科医生对风险分层患者进行分层,并指导化疗预防决策.
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