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时间:特定时间的机器学习评估,以优化超大规模输血.

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概括
此摘要是机器生成的。

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科学领域:

  • 创伤的复苏创伤的复苏
  • 机器学习在医学中的应用
  • 流血性休克管理的管理.

背景情况:

  • 超大规模输血 (UMT) 对创伤患者至关重要,但死亡率高.
  • 没有确定UMT的输血上限或徒劳点.
  • 特定时间的患者数据对UMT决策的影响还未得到充分研究.

研究的目的:

  • 用特定时间的机器学习预测接受UMT的创伤患者的死亡率.
  • 确定在UMT期间与生存相关的参数.

主要方法:

  • 对符合UMT标准的I级创伤患者 (2018-2021) 的回顾性审查 (≥20个红细胞产品/24小时).
  • 从血液银行,创伤记录和电子医疗记录中收集的数据.
  • 开发了特定时间的决策树模型来预测死亡率.

主要成果:

  • 包括180名患者;48小时死亡率为40.5%,整体为52.2%.
  • 已故的患者接受的血液制品更多 (中位数为71.5个对比55.5个单位).
  • 具体时间模型预测死亡率的准确率为81%;早期预测因素包括血液动力学和损伤严重程度,后来的预测因素包括pH和乳酸盐.

结论:

  • 停止UMT的决定取决于综合的患者和特定时间的数据,而不仅仅是单位体积.
  • 机器学习有效地模拟UMT生存因素.
  • 需要进一步的研究来完善和验证特定时间的决策树模型.