Department of Mechanical Engineering, University of Canterbury, New Zealand.
GIGA Cardiovascular Science, University of Liege, Liege, Belgium.
Comput Methods Programs Biomed. 2018 Oct;165:77-87. doi: 10.1016/j.cmpb.2018.08.004. Epub 2018 Aug 10.
Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies.
This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms.
Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmHO.
The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV.
机械通气（MV）是急性呼吸衰竭患者的主要治疗方法。然而，由于健康和受损肺泡的异质性，选择不当的呼吸机设置可能会导致进一步的肺损伤。将呼气末正压（PEEP）调整到最小顺应性点是一种肺保护性通气策略。然而，即使低水平的 PEEP 也可能导致肺部组织高度炎症的个体发生呼吸机相关性肺损伤。因此，能够准确预测 PEEP 变化后吸气峰压的模型可以提高临床医生对尝试潜在有益治疗策略的信心。
本研究开发并验证了一种生理相关的呼吸模型，该模型通过基础函数在经过良好验证的单室肺模型中捕获顺应性和阻力。该模型可以使用低 PEEP 下可用的信息进行个性化设置，以预测较高 PEEP 下的肺力学。使用来自 4 名患者和 8 个募集操作臂的数据进行概念验证。
结果表明，在预测向上的临床相关压力范围内，误差较低，模型能够以低于 10%的误差预测吸气峰压，在高达 12cmH2O 的 PEEP 变化范围内，预测成功率超过 90%。
研究结果提供了一种基于模型的计算方法，可以预测 MV 治疗变化时的临床相关反应，这是第一个 MV 虚拟患者的基础。
