预测机械通风的成功断奶,通过降低积极的末尾呼气压力水平,使用机器学习来预测
Seyedmostafa Sheikhalishahi1, Mathias Kaspar1, Sarra Zaghdoudi1
1Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
PLOS digital health
|March 27, 2024
概括
机器学习模型可以通过针对正极呼气压 (PEEP) 变化来预测成功的机械通风断奶. 这种方法有助于临床医生做出呼吸机管理决策,可能改善患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
背景情况:
- 机械通风 (MV) 断奶是复杂的,影响患者的结果和成本.
- 目前的断奶方法缺乏标准化,长时间的MV与不良事件有关.
- 预测断奶准备是具有挑战性的,正极呼气压 (PEEP) 作为预测目标未得到充分利用.
研究的目的:
- 开发和验证一种机器学习模型,用于预测机械通风的成功断奶.
- 使用持续的正极呼气压 (PEEP) 降低作为预测断奶成功的目标.
- 用既定指标评估模型的性能,并确定关键的预测变量.
主要方法:
- 从MIMIC-IV和eICU-CRD数据库中对12,153名患者进行了回顾性分析.
- 开发极端梯度提升和物流回归模型,以持续减少PEEP为目标.
- 模型评估使用AUROC,AUPRC,F1-Score,回忆,PPV和NPV;通过SHAP评估的变量重要性.
主要成果:
- 最好的模型实现了0.84的AUROC和0.69的AUPRC.
- 高预测性表现显示,回忆值为0.85,F1得分为0.86,PPV为0.87.
- SHAP分析确定了临床相关的变量,如MV持续时间,SaO2,PEEP和GCS组件.
结论:
- 机器学习模型在预测基于PEEP减少的成功机械通风断奶方面表现有希望.
- 开发的模型具有很高的积极预测价值,可以帮助临床医生做出呼吸机管理决策.
- 这种方法提供了一种新的策略,可以优化断奶协议,并可能降低医疗保健成本.
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