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The knowns and unknowns of perfusion disturbances in COVID-19 pneumonia
Critical Care ( IF 15.1 ) Pub Date : 2021-09-28 , DOI: 10.1186/s13054-021-03742-y
Mattia Busana 1 , Lorenzo Giosa 2
Affiliation  

Editor,

Ball et al. [1] recently proposed a thoughtful quantitative analysis of gas and blood distribution in the lungs of patients with severe COVID-19 pneumonia. The paper offers some interesting cues that we consider worth discussing.

First, the images of dual-energy CT scan (DECT) were used as surrogates of the ventilation and perfusion distribution in the lung. However, other imaging techniques, like scintigraphy and SPECT, might be more suitable for this task. Indeed, while perfusion is likely accurately depicted by DECT, ventilation is not. Indeed, for at least two reasons, the gas content measured by this technique (notably during tidal breathing and not at constant airway pressure) may not represent a good surrogate of the distribution of ventilation:

  1. 1.

    Airway closure may dissociate the instantaneous gas volume from the actual ventilation, and the phenomenon may be more common than currently thought [2];

  2. 2.

    The opening pressures cannot be investigated by a single CT scan [3]. It follows that undetected recruitability (possibly nonlinear along the pressure–volume curve) may contribute to the uncoupling between measured gas volume and alveolar ventilation.

This leads, in our opinion, to some inconsistencies. Most importantly, in Fig. 4, the authors show a plot reasonably inspired by the VA/QT distributions obtained with the multiple inert gas elimination technique. Unfortunately, such distribution is likely far from what we may expect from COVID-19 pneumonia. Indeed, the displayed distributions are essentially centered on a gas–blood volume ratio ~ 1. However, as we have shown in our theoretical model [4], such distribution (coupled with the relatively low shunt fraction reported in Table 2) is not compatible with the severe hypoxemia observed in these patients.

As a side note, considering that the analysis performed by Ball et al. excluded large vessels, one may wonder how the presence of abnormal vasodilation and vascular anastomoses [5] would affect their results. Indeed, nonfunctional vessels in a “gas-rich” region may escape the definition of shunt used by the authors.

The impact of perfusion alterations on gas exchange in COVID-19 is far from being understood. Despite the aforementioned limitations, Ball et al. must be congratulated for their effort in reporting compelling data that represent a smart step forward in the understanding and, most importantly, in the quantification of the problem.

  • Lorenzo Ball,
  • Chiara Robba,
  • Jacob Herrmann,
  • Sarah E. Gerard,
  • Yi Xin,
  • Maura Mandelli,
  • Denise Battaglini,
  • Iole Brunetti,
  • Giuseppe Minetti,
  • Sara Seitun,
  • Giulio Bovio,
  • Antonio Vena,
  • Daniele Roberto Giacobbe,
  • Matteo Bassetti,
  • Patricia R. M. Rocco,
  • Maurizio Cereda,
  • Rahim R. Rizi,
  • Lucio Castellan,
  • Nicolò Patroniti &
  • Paolo Pelosi 

We would like to thank Dr. Busana and Dr. Giosa for their interest in our study [1] and for giving us the opportunity to expand the discussion regarding disturbances of lung perfusion in COVID-19 patients. They highlighted the differences between ventilation and static aeration as assessed by CT; questioned the interpretation of our gas:blood ratio distribution plots and the definition of shunt, comparing our in vivo results with those of their computational theoretical model [4]. As per local practice during the COVID-19 pandemic, CT scans were acquired during breath-hold and we used lung aeration to define shunt, dead space, and non-aerated/non-perfused areas. Lung aeration may be considered as a potential surrogate of ventilation, especially when protective tidal volume and limited inspiratory plateau pressure are used, as in the case of COVID-19 mechanically ventilated patients [6]. In this context, it seems reasonable to assume that non-aerated areas are also non-ventilated and that aerated regions receive a certain amount of ventilation, especially given the small extent of hyper-aeration and poor recruitment with application of PEEP observed in these patients [7]. The pulmonary gas:blood volume ratio was defined as:

$${\text{Gas}}{:}\;{\text{Blood}}\;{\text{volume}}\;{\text{ratio}} = \frac{{f_{{\text{A}}} \cdot \overline{{{\text{PBV}}}} }}{{{\text{PBV}} \cdot \overline{{f_{{\text{A}}} }} }}$$

namely as the ratio of mean-normalized gas fraction to mean-normalized pulmonary blood volume: based on this definition, the values of the ratio are centered around 1 in each patient and cannot be directly translated into V′A/Q′T without knowing the cardiac output and alveolar minute ventilation. Such a ratio allows within-patient comparisons of the relative distribution of aeration and perfusion. We excluded only large lung vessels from the analysis: the vasodilation of peripheral vessels affects V′A/Q′T at an anatomical scale well below the size of one CT voxel [8]: voxels comprising dilated vessels and patent alveoli would have an intermediate CT attenuation value and a high pulmonary blood volume, corresponding to our “gas:blood ratio < 1” compartment.

Despite several technical limitations of the technique we proposed, our overall interpretation of the data is that in the early stages of the disease, hypoxia is explained by low V′A/Q′T responsive to higher inspiratory oxygen fractions and non-invasive respiratory assistance, while also by increased true shunt in the late stages requiring invasive mechanical ventilation.

In conclusion, we believe that our data should not be interpreted as in contrast with your theoretical model, but rather could represent its confirmation and an attempt of explaining in vivo gas-exchange changes in critically ill patients with severe COVID-19 pneumonia.

NA.

DECT:

Dual-energy CT scan

SPECT:

Single photon emission computed tomography

VA/QT :

Ventilation–perfusion distribution

  1. 1.

    Ball L, Robba C, Herrmann J, Gerard SE, Xin Y, Mandelli M, et al. Lung distribution of gas and blood volume in critically ill COVID-19 patients: a quantitative dual-energy computed tomography study. Crit Care. 2021;25:214.

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  2. 2.

    Hedenstierna G, Chen L, Brochard L. Airway closure, more harmful than atelectasis in intensive care? Intensive Care Med. 2020;46:2373–6.

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  3. 3.

    Cressoni M, Chiumello D, Algieri I, Brioni M, Chiurazzi C, Colombo A, et al. Opening pressures and atelectrauma in acute respiratory distress syndrome. Intensive Care Med. 2017;43:603–11.

    Article Google Scholar

  4. 4.

    Busana M, Giosa L, Cressoni M, Gasperetti A, Di Girolamo L, Martinelli A, et al. The impact of ventilation-perfusion inequality in COVID-19: a computational model. J Appl Physiol. 2021;130(3):865–76. https://doi.org/10.1152/japplphysiol.00871.2020.

    CAS Article PubMed PubMed Central Google Scholar

  5. 5.

    Galambos C, Bush D, Abman SH. Intrapulmonary bronchopulmonary anastomoses in COVID-19 respiratory failure. Eur Respir J. 2021;2004397.

  6. 6.

    Gattinoni L, Pelosi P, Crotti S, Valenza F. Effects of positive end-expiratory pressure on regional distribution of tidal volume and recruitment in adult respiratory distress syndrome. Am J Respir Crit Care Med. 1995;151:1807–14.

    CAS Article Google Scholar

  7. 7.

    Ball L, Robba C, Maiello L, Herrmann J, Gerard SE, Xin Y, et al. Computed tomography assessment of PEEP-induced alveolar recruitment in patients with severe COVID-19 pneumonia. Crit Care Lond Engl. 2021;25:81.

    Article Google Scholar

  8. 8.

    Townsley MI. Structure and composition of pulmonary arteries, capillaries, and veins. Comp Physiol. 2012;2:675–709.

    Article Google Scholar

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None.

NA.

Affiliations

  1. Department of Anesthesiology, University Medical Center Göttingen, Robert Koch Straße 40, 37075, Göttingen, Germany

    Mattia Busana

  2. Department of Surgical Sciences, University of Turin, Turin, Italy

    Lorenzo Giosa

Authors
  1. Mattia BusanaView author publications

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  2. Lorenzo GiosaView author publications

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Contributions

Both authors participated in the writing of the present letter.

Corresponding author

Correspondence to Mattia Busana.

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NA.

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All authors approved the manuscript in the current form.

Competing interests

The authors have no conflicts of interest to disclose.

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Busana, M., Giosa, L. The knowns and unknowns of perfusion disturbances in COVID-19 pneumonia. Crit Care 25, 352 (2021). https://doi.org/10.1186/s13054-021-03742-y

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中文翻译:

COVID-19 肺炎灌注障碍的已知和未知

编辑,

球等。[1] 最近提出了对重症 COVID-19 肺炎患者肺部气体和血液分布的深思熟虑的定量分析。这篇论文提供了一些我们认为值得讨论的有趣线索。

首先,双能 CT 扫描 (DECT) 的图像被用作肺中通气和灌注分布的替代物。然而,其他成像技术,如闪烁扫描和 SPECT,可能更适合这项任务。事实上,虽然 DECT 可能准确地描述了灌注,但通气不是。事实上,至少出于两个原因,通过这种技术测量的气体含量(特别是在潮汐呼吸期间而不是在恒定气道压力下)可能不能很好地代表通气分布:

  1. 1.

    气道闭合可能会使瞬时气体量与实际通气量分离,这种现象可能比目前认为的更为普遍[2];

  2. 2.

    单次 CT 扫描无法研究开启压力 [3]。因此,未检测到的肺复张(可能沿压力-容积曲线呈非线性)可能有助于测量气体容积和肺泡通气量之间的解耦。

在我们看来,这导致了一些不一致。最重要的是,在图 4 中,作者展示了一个由多惰性气体消除技术获得的 V A /Q T分布合理启发的图。不幸的是,这种分布可能与我们对 COVID-19 肺炎的预期相去甚远。事实上,显示的分布基本上集中在气血体积比 ~ 1。然而,正如我们在我们的理论模型 [4] 中所示,这种分布(加上表 2 中报告的相对较低的分流分数)是不兼容的在这些患者中观察到严重的低氧血症。

作为旁注,考虑到 Ball 等人进行的分析。排除大血管,人们可能想知道异常血管舒张和血管吻合 [5] 的存在如何影响他们的结果。事实上,“富含气体”区域中的非功能性血管可能不符合作者使用的分流定义。

灌注改变对 COVID-19 气体交换的影响尚不清楚。尽管有上述限制,Ball 等人。必须祝贺他们在报告令人信服的数据方面所做的努力,这些数据代表了在理解方面向前迈出的明智一步,最重要的是,在问题的量化方面。

  • 洛伦佐·鲍尔
  • 基亚拉·罗巴
  • 雅各布·赫尔曼
  • 莎拉·E·杰拉德,
  • 易欣,
  • 毛拉·曼德利
  • 丹尼斯·巴塔格利尼
  • 伊奥莱·布鲁内蒂
  • 朱塞佩·米内蒂
  • 萨拉·塞顿
  • 朱利奥·博维奥
  • 安东尼奥·维纳
  • 丹尼尔·罗伯托·贾科布,
  • 马泰奥·巴塞蒂
  • 帕特里夏·RM·罗科,
  • 毛里齐奥·塞雷达
  • Rahim R. Rizi,
  • 卢西奥·卡斯特兰
  • 尼科洛·帕特罗尼蒂 &
  • 保罗佩洛西 

我们要感谢 Busana 博士和 Giosa 博士对我们的研究 [1] 的兴趣,并感谢他们让我们有机会扩大关于 COVID-19 患者肺灌注紊乱的讨论。他们强调了 CT 评估的通风和静态通风之间的差异;质疑我们的气体:血液比率分布图的解释和分流的定义,将我们的体内结果与其计算理论模型的结果进行比较 [4]。根据 COVID-19 大流行期间的当地惯例,在屏气期间采集 CT 扫描,我们使用肺通气来定义分流、死腔和非充气/非灌注区域。肺通气可被视为通气的潜在替代物,尤其是在使用保护性潮气量和有限的吸气平台压时,就像 COVID-19 机械通气患者的情况 [6]。在这种情况下,假设非通气区域也未通气并且通气区域接受一定量的通气似乎是合理的,特别是考虑到在这些患者中观察到应用 PEEP 时过度通气的程度小和募集不良[7]。肺气:血容量比定义为:

$${\text{Gas}}{:}\;{\text{Blood}}\;{\text{volume}}\;{\text{ratio}} = \frac{{f_{{\text{ A}}} \cdot \overline{{{\text{PBV}}}} }}{{{\text{PBV}} \cdot \overline{{f_{{\text{A}}} }} }} $$

即平均归一化气体分数与平均归一化肺血容量的比值:根据此定义,每个患者的该比值均以 1 为中心,并且不能在不知道的情况下直接转换为 V' A / Q' T心输出量和肺泡分钟通气量。这样的比率允许在患者内比较通气和灌注的相对分布。我们仅从分析中排除了大肺血管:外周血管的血管舒张在远低于一个 CT 体素大小的解剖尺度上影响 V' A / Q' T [8]:包含扩张血管和未闭肺泡的体素将具有中间CT 衰减值和高肺血容量,对应于我们的“气血比 <  1英寸隔间。

尽管我们提出的技术存在一些技术限制,但我们对数据的总体解释是,在疾病的早期阶段,低氧是由低 V' A / Q' T对更高的吸入氧分数和无创呼吸辅助做出反应来解释的,同时也在需要有创机械通气的晚期增加真正的分流。

总之,我们认为我们的数据不应被解释为与您的理论模型相反,而是可以代表其确认并尝试解释重症 COVID-19 肺炎重症患者体内气体交换的变化。

不适用。

检测:

双能CT扫描

观察:

单光子发射计算机断层扫描

V A / Q T :

通气-灌注分布

  1. 1.

    Ball L、Robba C、Herrmann J、Gerard SE、Xin Y、Mandelli M 等。危重 COVID-19 患者的肺气体和血容量分布:定量双能计算机断层扫描研究。暴击护理。2021;25:214。

    文章 谷歌学术

  2. 2.

    Hedenstierna G、Chen L、Brochard L。气道闭合,比重症监护中的肺不张更有害?重症监护医学。2020;46:2373-6。

    文章 谷歌学术

  3. 3.

    Cressoni M、Chiumello D、Algieri I、Brioni M、Chiurazzi C、Colombo A 等。急性呼吸窘迫综合征中的开放压力和肺外伤。重症监护医学。2017;43:603-11。

    文章 谷歌学术

  4. 4.

    Busana M、Giosa L、Cressoni M、Gasperetti A、Di Girolamo L、Martinelli A 等。COVID-19 中通气-灌注不平等的影响:计算模型。J 应用生理学。2021;130(3):865–76。https://doi.org/10.1152/japplphysiol.00871.2020。

    CAS 文章 PubMed PubMed Central Google Scholar

  5. 5.

    加兰博斯 C、布什 D、阿布曼 SH。COVID-19呼吸衰竭的肺内支气管肺吻合术。Eur Respir J. 2021;2004397。

  6. 6.

    Gattinoni L、Pelosi P、Crotti S、Valenza F. 呼气末正压对成人呼吸窘迫综合征中潮气量区域分布和募集的影响。Am J Respir Crit Care Med。1995;151:1807-14。

    CAS 文章 Google Scholar

  7. 7.

    Ball L、Robba C、Maiello L、Herrmann J、Gerard SE、Xin Y 等。严重 COVID-19 肺炎患者 PEEP 诱导肺泡复张的计算机断层扫描评估。Crit Care 伦敦英语。2021;25:81。

    文章 谷歌学术

  8. 8.

    汤斯利 MI。肺动脉、毛细血管和静脉的结构和组成。比较生理学。2012;2:675-709。

    文章 谷歌学术

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隶属关系

  1. 麻醉科,哥廷根大学医学中心,Robert Koch Straße 40, 37075, Göttingen, Germany

    马蒂亚·布萨纳

  2. 意大利都灵都灵大学外科科学系

    洛伦佐·乔萨

作者
  1. Mattia Busana查看作者出版物

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  2. Lorenzo Giosa查看作者出版物

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Busana, M., Giosa, L. COVID-19 肺炎灌注障碍的已知和未知。暴击护理 25, 352 (2021)。https://doi.org/10.1186/s13054-021-03742-y

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