Full-lung simulations of mechanically ventilated lungs incorporating recruitment/derecruitment dynamics

Haoran Ma, Hideki Fujioka, David Halpern, Jason H. T. Bates, Donald P. Gaver
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Abstract

This study developed and investigated a comprehensive multiscale computational model of a mechanically ventilated ARDS lung to elucidate the underlying mechanisms contributing to the development or prevention of VILI. This model is built upon a healthy lung model that incorporates realistic airway and alveolar geometry, tissue distensibility, and surfactant dynamics. Key features of the ARDS model include recruitment and derecruitment (RD) dynamics, alveolar tissue viscoelasticity, and surfactant deficiency. This model successfully reproduces realistic pressure-volume (PV) behavior, dynamic surface tension, and time-dependent descriptions of RD events as a function of the ventilation scenario. Simulations of Time-Controlled Adaptive Ventilation (TCAV) modes, with short and long durations of exhalation ( T Low - and T Low + , respectively), reveal a higher incidence of RD for T Low + despite reduced surface tensions due to interfacial compression. This finding aligns with experimental evidence emphasizing the critical role of timing in protective ventilation strategies. Quantitative analysis of energy dissipation indicates that while alveolar recruitment contributes only a small fraction of total energy dissipation, its spatial concentration and brief duration may significantly contribute to VILI progression due to its focal nature and higher intensity. Leveraging the computational framework, the model may be extended to facilitate the development of personalized protective ventilation strategies to enhance patient outcomes. As such, this computational modeling approach offers valuable insights into the complex dynamics of VILI that may guide the optimization of ventilation strategies in ARDS management.
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机械通气肺的全肺模拟,包括招募/退出招募动力学
本研究开发并研究了机械通气ARDS肺的综合多尺度计算模型,以阐明促进VILI发展或预防的潜在机制。这个模型是建立在一个健康的肺模型,结合现实的气道和肺泡几何形状,组织扩张性和表面活性剂动力学。ARDS模型的主要特征包括再招募和再招募(RD)动力学、肺泡组织粘弹性和表面活性剂缺乏。该模型成功地再现了真实的压力-体积(PV)行为、动态表面张力和随时间变化的RD事件描述,作为通风场景的函数。时间控制自适应通气(TCAV)模式的模拟显示,在短时间和长时间呼气(分别为T Low -和T Low +)的情况下,尽管由于界面压缩导致表面张力降低,但T Low +的RD发生率更高。这一发现与强调时间在保护性通气策略中的关键作用的实验证据相一致。能量耗散的定量分析表明,虽然肺泡补充只占总能量耗散的一小部分,但由于其聚焦性和较高的强度,其空间集中和持续时间短可能显著促进VILI的进展。利用计算框架,该模型可以扩展,以促进个性化保护性通气策略的发展,以提高患者的预后。因此,这种计算建模方法为VILI的复杂动力学提供了有价值的见解,可以指导ARDS管理中通气策略的优化。
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