College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK.
IEEE Trans Biomed Eng. 2013 Jun;60(6):1599-607. doi: 10.1109/TBME.2013.2239645. Epub 2013 Jan 11.
The selection of mechanical ventilator settings that ensure adequate oxygenation and carbon dioxide clearance while minimizing the risk of ventilator-associated lung injury (VALI) is a significant challenge for intensive-care clinicians. Current guidelines are largely based on previous experience combined with recommendations from a limited number of in vivo studies whose data are typically more applicable to populations than to individuals suffering from particular diseases of the lung. By combining validated computational models of pulmonary pathophysiology with global optimization algorithms, we generate in silico experiments to examine current practice and uncover optimal combinations of ventilator settings for individual patient and disease states. Formulating the problem as a multiobjective, multivariable constrained optimization problem, we compute settings of tidal volume, ventilation rate, inspiratory/expiratory ratio, positive end-expiratory pressure and inspired fraction of oxygen that optimally manage the tradeoffs between ensuring adequate oxygenation and carbon dioxide clearance and minimizing the risk of VALI for different pulmonary disease scenarios.
选择既能确保充分氧合又能清除二氧化碳，同时又能将呼吸机相关性肺损伤（VALI）风险降至最低的机械通气设置，是重症监护临床医生面临的一项重大挑战。目前的指南主要基于以往的经验，并结合了为数不多的体内研究的建议，而这些研究的数据通常更适用于人群，而不是患有特定肺部疾病的个体。通过将经过验证的肺部病理生理学计算模型与全局优化算法相结合，我们生成了计算机实验，以检查当前的实践，并为个体患者和疾病状态发现最佳的呼吸机设置组合。通过将该问题表述为一个多目标、多变量约束优化问题，我们计算出潮气量、通气率、吸/呼比、呼气末正压和吸入氧分数的设置，这些设置可以在不同的肺部疾病情况下，在确保充分氧合和清除二氧化碳以及最小化 VALI 风险之间进行最佳权衡。
