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术内特征改善了冠状动脉旁路移植后的模型风险预测.

Willa Potosnak1, Keith A Dufendach1,2, Chirag Nagpal1

  • 1Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.

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科学领域:

  • 心胸外科手术 心胸外科手术
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 在冠状动脉旁路移植 (CABG) 后发生不良术后事件的当前风险模型不包括手术内生理参数.
  • 术内数据具有潜在的预测价值,用于识别患有并发症风险较高的患者.

研究的目的:

  • 评估是否结合连续的手术内数据可以提高机器学习模型对CABG后各种不良结果的预测.
  • 评估预测30天死亡率,衰竭,再手术,长时间通风以及联合发病率和死亡率 (MM) 的改善.

主要方法:

  • 来自胸部外科医生协会 (STS) 数据库的综合数据与追溯的连续手术内患者数据.
  • 开发了使用5倍交叉验证的后勤回归模型,结合了手术内特征和STS手术前风险评分.

主要成果:

  • 与STS风险计算器单独相比,集成手术内特征和STS风险得分的模型显示,长时间通风和MM的预测性能 (AUC) 得到了改善.
  • 当包括手术内数据时,观察到长时间通风和MM的增强校准,由较低的布赖尔分数表明.

结论:

  • 将时间序列的手术内数据集成到风险模型中,可以显著改善手术后不良事件的预测.
  • 这些增强模型可能有助于更早地识别高风险患者,从而使术后监测更加密切,并改善患者护理.