通过虚拟患者模型中的个性化基础函数来预测肺应答
Trudy Caljé-van der Klei1, Qianhui Sun2, J Geoffrey Chase1
1Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
Computer methods and programs in biomedicine
|January 3, 2024
概括
这项研究引入了一个指数基础函数,用于预测机械通风期间的肺机械. 这种方法准确地模拟了肺部招募和膨胀,改善了患者特定的正端呼吸压力 (PEEP) 设置.
科学领域:
- 生理学 生理学 生理学
- 机械通风机械通风机械通风
- 计算机建模 计算建模
背景情况:
- 使用正端呼气压 (PEEP) 的招募机动可以提高肺体积,但缺乏标准化,患者特定的设置.
- 最佳的PEEP水平随患者的病情而波动,需要个性化监测.
- 目前用于确定PEEP的方法尚未完全标准化.
研究的目的:
- 开发和验证与生理相关的基础函数集,用于预测虚拟患者模型中的肺弹性演变.
- 纳入用于建模和预测拉伸弹性的新元素.
- 为了提高机械通风 (MV) 肺机械学预测的准确性和稳定性.
主要方法:
- 检查了3个基本函数集 (指数式,抛物线式,累积式) 用于预测肺部力学.
- 从18名控制体积通风 (VCV) 和14名控制压力通风 (PCV) 患者的招募机动数据中验证了预测.
- 在各种基线PEEP水平中,增加PEEP的评估预测准确度高达12cmH2O.
主要成果:
- 指数基础函数在VCV和PCV模式中表现出优异的性能,与其他组相比.
- 在VCV患者中,绝对峰值吸气压 (PIP) 预测误差的中位数为1.63cmH2O.
- 在PCV患者中,平均峰值启发体积 (PIV) 预测误差为0.028 L,而R2 = 0.90-0.95用于膨胀预测.
结论:
- 一个指数基础函数最好地捕捉到不同机械通风模式的肺部招募机制.
- 这种模型首次准确地预测了在5-10%的准确度内膨胀力学.
- 经过验证的数字双胞胎模型可以在改变呼吸机设置之前更好地预测肺损伤风险.
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