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机器学习模型预测器的脑内组织等离子体激活器和DNase失败在肺部感染:一个多中心研究
Danai Khemasuwan1, Candice Wilshire2, Chakravarthy Reddy3
1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Virginia Commonwealth University Health System, Richmond, Virginia.
Annals of the American Thoracic Society
|October 29, 2024
概括
在复杂的肺/肺中,肺内酶疗法 (IET) 失败的预测是腹/结性肺炎和肺厚化. 这些发现可以帮助优化肺部感染的治疗策略.
科学领域:
- 肺部病理学 肺部病理学
- 胸部外科手术 胸部外科手术
- 医疗信息学 医疗信息学
背景情况:
- 使用组织等离子激素激活剂 (tPA) 和DNase的脑内酶疗法 (IET) 可以减少复杂的肺/肺 (CPPE/肺) 的手术干预.
- IET的失败可能导致治疗延迟和长时间住院.
研究的目的:
- 为了确定临床和放射性风险因素,预测在CPE/瘤患者中IET的失败.
- 为了比较不同机器学习模型在预测IET失败方面的表现.
主要方法:
- 这是一项多中心的回顾性研究,涉及466名患者,他们因肺部感染而接受IET治疗.
- 四个机器学习分类器的比较:L1-惩罚后勤回归,支持矢量机 (SVM),极端梯度增强 (XGBoost) 和光梯度增强机 (LightGBM).
- 评估模型性能使用启动时验证的指标,包括F-β,并评估变量的重要性.
主要成果:
- 在78%的患者中,IET成功地解决了CPE/empyema.
- 支持矢量机 (SVM) 模型表现出卓越的性能.
- 在多个机器学习模型中,腹/死性肺炎和腹厚始终被确定为IET失败的最强预测因素.
结论:
- 腹/死性肺炎和多叶膜加厚是CPPE/empyema中IET失败的重要预测因素.
- 机器学习模型可以有效地识别IET失败的风险因素.
- 建议在更大的研究中进一步验证以确认这些发现.
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