Investigating Strategies for Mechanical Ventilation in COVID-19 via Computational Simulation of Virtual Patients

COVID-19患者机械通气的初步临床指南建议采用治疗急性呼吸窘迫综合征（ARDS）的标准方法。然而，新出现的临床经验表明，COVID-19肺炎患者可能表现为非典型形式的综合征，其特征（至少在最初）为严重低氧血症，肺力学（即保留的肺顺应性）相对良好-这在ARDS中很少见[1]。这种疾病表型的病理生理学基础目前尚不清楚。最近的一项研究还指出，COVID-19患者存在显著的时间相关性疾病谱，至少有两种潜在的“亚表型”：L型，其特征在于低弹性（即高顺应性）、低通气灌注比、低肺重量和低影像学可复张性; H型，其特征在于高弹性、高右向左分流、高肺重量和高复张性[2]。目前尚不清楚某些患者从L型转变为H型的主要原因是COVID-19肺炎的演变，还是L型阶段的损伤性机械通气导致的肺损伤。这些问题（以及其他问题）将需要使用实验（动物）模型进行广泛的研究，并最终在人类患者中进行临床试验。然而，临床医生现在需要深入了解COVID-19可能的潜在疾病病理生理学，以便制定适当的策略为患者的肺部通气。我们建议采用最先进的计算模拟器，该模拟器已被开发用于研究ARDS中的机械通气[3-4]，以研究COVID-19患者机械通气特有的一系列问题。

Initial clinical guidelines for mechanical ventilation of COVID-19 patients suggested following standard approaches used in the treatment of acute respiratory distress syndrome (ARDS). However, emerging clinical experience suggests that patients with COVID-19 pneumonia may present an atypical form of the syndrome, characterised (at least initially) by severe hypoxemia with relatively well-preserved lung mechanics (i.e. preserved lung compliance) - a combination that is rarely seen in ARDS [1]. The pathophysiological basis for this disease phenotype is currently unclear. A recent study also noted a significant time-related disease spectrum in COVID-19 patients, with at least two potential "sub-phenotypes": Type L, characterized by low elastance (i.e. high compliance), low ventilation to perfusion ratio, low lung weight and low recruitability by imaging; and a Type H, characterized by high elastance, high right-to-left shunt, high lung weight and high recruitability [2]. It is currently unclear whether a transition from Type L to Type H in some patients is due primarily to the evolution of the COVID-19 pneumonitis or to lung injury caused by injurious mechanical ventilation during the Type L phase. These issues (and others) will require extensive investigation using experimental (animal) models and ultimately clinical trials in human patients. However, clinicians need insights into the possible underlying disease pathophysiology of COVID-19 now, in order to develop appropriate strategies for ventilating patients' lungs. We propose adapting a state-of-the-art computational simulator, which has been developed to investigate mechanical ventilation in ARDS [3-4], to investigate a range of issues that are specific to mechanical ventilation of COVID-19 patients.