Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
PLoS One. 2022 Aug 30;17(8):e0273214. doi: 10.1371/journal.pone.0273214. eCollection 2022.
Busana et al. (doi.org/10.1152/japplphysiol.00871.2020) published 5 patients with COVID-19 in whom the fraction of non-aerated lung tissue had been quantified by computed tomography. They assumed that shunt flow fraction was proportional to the non-aerated lung fraction, and, by randomly generating 106 different bimodal distributions for the ventilation-perfusion ([Formula: see text]) ratios in the lung, specified as sets of paired values {[Formula: see text]}, sought to identify as solutions those that generated the observed arterial partial pressures of CO2 and O2 (PaCO2 and PaO2). Our study sought to develop a direct method of calculation to replace the approach of randomly generating different distributions, and so provide more accurate solutions that were within the measurement error of the blood-gas data. For the one patient in whom Busana et al. did not find solutions, we demonstrated that the assumed shunt flow fraction led to a non-shunt blood flow that was too low to support the required gas exchange. For the other four patients, we found precise solutions (prediction error < 1x10-3 mmHg for both PaCO2 and PaO2), with distributions qualitatively similar to those of Busana et al. These distributions were extremely wide and unlikely to be physically realisable, because they predict the maintenance of very large concentration gradients in regions of the lung where convection is slow. We consider that these wide distributions arise because the assumed value for shunt flow is too low in these patients, and we discuss possible reasons why the assumption relating to shunt flow fraction may break down in COVID-19 pneumonia.
布萨纳等人(doi.org/10.1152/japplphysiol.00871.2020)发表了 5 例 COVID-19 患者的资料,这些患者的非充气肺组织部分已通过计算机断层扫描进行了量化。他们假设分流量与非充气肺部分成比例,并且通过随机生成 106 种不同的通气-灌注([Formula: see text])比值的双峰分布,将其指定为配对值集{[Formula: see text]},试图找到那些可以生成观察到的动脉二氧化碳分压(PaCO2)和氧分压(PaO2)的解决方案。我们的研究旨在开发一种直接的计算方法来替代随机生成不同分布的方法,从而提供更准确的解决方案,这些解决方案在血气数据的测量误差范围内。对于布萨纳等人未找到解决方案的一位患者,我们证明了所假设的分流量导致非分流血流过低,无法支持所需的气体交换。对于其他四名患者,我们找到了精确的解决方案(PaCO2 和 PaO2 的预测误差均小于 1x10-3 mmHg),分布与布萨纳等人的分布定性相似。这些分布非常宽,不太可能在物理上实现,因为它们预测了在对流缓慢的肺部区域中保持非常大的浓度梯度。我们认为这些宽分布是由于这些患者中假设的分流量过低所致,并讨论了 COVID-19 肺炎中与分流量分数相关的假设可能失效的可能原因。