机器学习方法用于预测经过多层脊柱后部仪器融合的患者中的急性损伤
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)
|October 14, 2024
概括
机器学习模型确定了脊髓融合后急性损伤 (AKI) 的关键风险因素. 术前对患有慢性病,高血压,糖尿病,老年或心力衰竭的患者进行鉴定,可以帮助减轻AKI风险.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 神经外科 神经外科
- 数据科学数据科学数据科学
背景情况:
- 急性损伤 (AKI) 是脊髓融合手术后的重大并发症,影响患者的治疗结果.
- 确定AKI风险因素对于外科手术意识和风险减轻策略至关重要.
研究的目的:
- 开发机器学习 (ML) 模型,用于评估后脊椎仪器融合后患有AKI的患者风险因素.
主要方法:
- 利用IBM MarketScan数据库 (2009-2021) 对接受脊髓融合 (3-6级,后部仪器) 的患者.
- 应用了ML模型,包括后勤回归,LSVM,随机森林,XGBoost和神经网络,以预测90天AKI发生率.
- 使用国际疾病分类代码对AKI定义进行调查的风险因素.
主要成果:
- 在141,697名患者中,90天的AKI总率为2.96%.
- 后勤回归和LSVM模型显示出最好的预测性能 (AUC 0.75).
- AKI的主要预测因素包括慢性病,高血压,糖尿病,年龄大于50岁和充血性心力衰竭.
结论:
- 确定了五个关键变量 (慢性病,高血压,糖尿病,年龄>50,充血性心力衰竭) 与AKI强烈相关.
- 开发了一个风险计算器,显示了更多风险因素 (OR 3.38 至 91.10) 的 AKI 几率增加.
- 这些发现有助于外科医生对风险分层的患者进行分层,并指导术后管理以减轻AKI.
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