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Dead space ventilation-related indices: bedside tools to evaluate the ventilation and perfusion relationship in patients with acute respiratory distress syndrome


Cumulative evidence has demonstrated that the ventilatory ratio closely correlates with mortality in acute respiratory distress syndrome (ARDS), and a primary feature in coronavirus disease 2019 (COVID-19)-ARDS is increased dead space that has been reported recently. Thus, new attention has been given to this group of dead space ventilation-related indices, such as physiological dead space fraction, ventilatory ratio, and end-tidal-to-arterial PCO2 ratio, which, albeit distinctive, are all global indices with which to assess the relationship between ventilation and perfusion. These parameters have already been applied to positive end expiratory pressure titration, prediction of responses to the prone position and the field of extracorporeal life support for patients suffering from ARDS. Dead space ventilation-related indices remain hampered by several deflects; notwithstanding, for this catastrophic syndrome, they may facilitate better stratifications and identifications of subphenotypes, thereby providing therapy tailored to individual needs.


A hallmark of classical ARDS is an increased shunt caused by alveolar collapse and/or alveolar flooding from a physiological viewpoint [1]. Over the past two decades, there has been increasing interest in dead space since the publication by Nuckton et al. in the early twenty-first century [2]. Indeed, the Berlin definition was based on \({\text{PaO}}_{2} /{\text{FiO}}_{2}\)(i.e., arterial partial pressure of \({\text{O}}_{2}\) to fraction of inspired \({\text{O}}_{2}\)) to classify patients into three categories, but its predictive power for mortality was far from perfect [3, 4]. Given that increased dead space was not uncommon in patients with ARDS and its association with reduced survival [2], \({\dot{\text{V}}\text{E}}_{{{\text{CORR}}}}\) (i.e., the corrected minute ventilation) (Tables 1 and 2) serving as a substitute for dead space, was used to define the severe ARDS subgroup in the draft Berlin definition; nevertheless, this failed. Thus, the final Berlin definition did not incorporate \({\dot{\text{V}}\text{E}}_{{{\text{CORR}}}}\) [3]. Moreover, dead space has been suggested to be predominant in COVID-19-ARDS [5]. Finally, a growing number of intuitive dead space ventilation-related indices with prognostic value have emerged [6, 7]. Therefore, attention has been redirected to these parameters that reflect ventilation and perfusion mismatch.

Table 1 Glossary of gas variables and notations
Table 2 Summary of dead space ventilation-related indices

This review covers three dead space ventilation-related indices that have attracted a great deal of attention. After a brief introduction, their current applications are described, and possible physiological rationales are unveiled. Despite several inevitable drawbacks, perhaps in the next decade, these parameters might be used in the subclassifications of ARDS based on severity and help to divide this heterogeneous syndrome into different subphenotypes to better guide personalized treatment management.

Dead space ventilation-related indices

Physiological dead space fraction

Dead space or physiological dead space (i.e., \({\text{VD}}_{{{\text{phys}}}}\)) is part of the volume that is ventilated but does not participate in gas exchange. \({\text{VD}}_{{{\text{phys}}}}\) can be divided into two components: airway dead space (i.e., \({\text{VD}}_{{{\text{aw}}}}\)) and alveolar dead space (i.e., \({\text{VD}}_{{{\text{alv}}}}\)). In mechanically ventilated patients, instrumental dead space (i.e., \({\text{VD}}_{{{\text{inst}}}}\)) which is the volume related to artificial airway could increase \({\text{VD}}_{{{\text{aw}}}}\). \({\text{VD}}_{{{\text{phys}}}}\) and its subcomponents are commonly expressed as the fraction of tidal volume to allow interpatient comparisons [12, 13]. Christian Bohr proposed a formula in 1891 to calculate dead space. Bohr’s dead space fraction (i.e., \({\text{VD}}_{{{\text{Bohr}}}} /{\text{VT}}\)) was calculated in the following manner: \({\text{VD}}_{{{\text{Bohr}}}} /{\text{VT}} = \frac{{{\text{PACO}}_{2} - {\text{P}}\overline{{\text{E}}} {\text{CO}}_{2} }}{{{\text{PACO}}_{2} }}\) (Table 2) [8]. In an ideal lung assuming all units with perfect \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching, \({\text{PACO}}_{2}\) is identical to \({\text{PaCO}}_{2}\) [14]. Thus, in 1938, Enghoff used \({\text{PaCO}}_{2}\) instead of \({\text{PACO}}_{2}\) to modify Bohr’s formula as follows: \({\text{VD}}_{{{\text{B}} - {\text{E}}}} /{\text{VT}} = \frac{{{\text{PACO}}_{2} - {\text{P}}\overline{{\text{E}}} {\text{CO}}_{2} }}{{{\text{PACO}}_{2} }}\) (Table 2) [9]. Enghoff’s modification of Bohr’s dead space fraction (i.e., \({\text{VD}}_{{{\text{B}} - {\text{E}}}} /{\text{VT}}\)) represents the physiological dead space fraction (\({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\)). However, on the one hand, the blood from \({\dot{\text{Q}}}_{{{\text{VA}}}} /{\dot{\text{Q}}}_{{\text{T}}}\) which consists of true shunt units (i.e., \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) = 0) and low \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) units could raise \({\text{PaCO}}_{2}\) [15]; thus, this substitution could increase error of calculating true dead space (i.e., \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}} = \infty\)) and high \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) units, on the other hand, using \(\frac{{{\text{PACO}}_{2} - {\text{P}}\overline{{\text{E}}} {\text{CO}}_{2} }}{{{\text{PACO}}_{2} }}\) considers all forms of \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch [16]. Therefore, \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) (i.e., \({\text{VD}}_{{{\text{B}} - {\text{E}}}} /{\text{VT}}\)) is a global index with which to assess \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch [12]. In 2002, Nuckton et al. first demonstrated that a high \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) was independently associated with an increased risk of death among patients with ARDS [2].

Ventilatory ratio

It is widely accepted that the presence of true dead space units (i.e., \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}} = \infty\)) and high \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) units could cause hypercapnia, and an increase in \({\dot{\text{V}}\text{E}}\) could facilitate \({\text{CO}}_{2}\) elimination to maintain an unchanged \({\text{PaCO}}_{2}\), which implies an association between \({\dot{\text{V}}\text{E}}\) and \({\text{PaCO}}_{2}\). Therefore, the ventilatory ratio (VR) was developed to better evaluate ventilatory efficiency. VR is described as \({\text{VR}} = \frac{{{\dot{\text{V}}\text{E}}_{{{\text{measured}}}} \times {\text{PaCO}}_{{2{\text{measured}}}} }}{{{\dot{\text{V}}\text{E}}_{{{\text{predicted}}}} \times {\text{PaCO}}_{{2{\text{predicted}}}} }}\) (Table 2). Likewise, VR reflects a continuous spectrum of \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch in the lung [6]. Not surprisingly, authors have also validated that there is an intimate correlation between VR and \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) [17,18,19,20,21], and most studies have concluded that a higher VR is a reliable indicator of mortality [17,18,19,20,21,22].

End-tidal-to-arterial PCO2 ratio

In the last century, authors calculated the ratio of alveolar dead space to alveolar tidal volume (i.e., \(\frac{{{\text{VD}}_{{{\text{alv}}}} }}{{{\text{VT}}_{{{\text{alv}}}} }}\)), which equals \(\frac{{{\text{P}}\left( {{\text{a}} - {\text{ET}}} \right){\text{CO}}_{2} }}{{{\text{PaCO}}_{2} }}\) and can be restated as \(1 - {\text{PETCO}}_{2} /{\text{PaCO}}_{2}\) [23]. Recently, Gattinoni et al. suggested that the end-tidal-to-arterial PCO2 ratio \(\left( {\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}} \right)\) (Table 2) deriving from \(\frac{{{\text{VD}}_{{{\text{alv}}}} }}{{{\text{VT}}_{{{\text{alv}}}} }}\) could be used as a bedside tool to monitor gas exchanges of patients with COVID-19-ARDS. \(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\) is also a global index with which to assess \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch, with a maximum value of 1. When this ratio approaches 1, it reflects an ameliorated gas exchange; conversely, deviation from 1 reflects gas exchange disturbance [7]. Later, authors established that there was a good correlation between \(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\) and \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\); in addition, a reduction in \(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\) was associated with a higher mortality risk in the non-COVID-19-ARDS and COVID-19-ARDS populations [24, 25].

Current applications

PEEP titration

Regarding PEEP titration, intensivists have gradually realized that it is not sufficient to target anatomical recruitment and/or improved oxygenation [26, 27], whereas targeting \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching may represent a promising approach. The gold standard to evaluate \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching is the multiple inert gas elimination technique (MIGET), which is complex [28]. Recently, authors using the automatic lung parameter estimator (ALPE) method found that after increases in PEEP, \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching exhibited heterogeneous responses [29]. Furthermore, based on electrical impedance tomography (EIT), Spinelli et al. reported that measurements of \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch allowed the identification of patients with a higher risk of death [30]. Thus, evaluation of \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching likely outperforms the methods that concentrate on anatomic recruitment (i.e., the methods based on respiratory mechanics and morphology) and the method according to oxygenation, thereby playing an important role in determining optimal PEEP.

Almost 50 years ago, Suter et al. first defined the optimal PEEP as that giving rise to the lowest \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) [31]. Furthermore, other studies demonstrated that indices such as the arterial minus end-tidal \({\text{CO}}_{2}\) gradient (i.e., \({\text{P}}\left( {{\text{a}} - {\text{ET}}} \right){\text{CO}}_{2}\)) and the ratio of alveolar dead space to alveolar tidal volume (i.e., \(\frac{{{\text{VD}}_{{{\text{alv}}}} }}{{{\text{VT}}_{{{\text{alv}}}} }}\)) were also helpful in PEEP titration [32,33,34,35,36]. These two indices are analogous to \(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\); thus, both are associated with dead space, or more specifically \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching. Altogether, employing these dead space ventilation-related parameters may help to titrate the optimal PEEP.

Prediction of response to the prone position

The PP can exert its impact even without ventilatory support, but the related risks cannot be ignored [37]. Thus, it is crucial to predict which patients with ARDS would benefit from the PP. Traditionally, \({\text{PaO}}_{2} /{\text{FiO}}_{2}\) was conceived of as a better indicator of a positive response to the PP [38]. In the landmark PROSEVA trial, Guerin and colleagues reported that prone positioning for an average of 16 h/d improved oxygenation and reduced the mortality of patients with ARDS by 50% [39]. Nonetheless, the correlation between enhanced survival and improved oxygenation was not significant [40]. Prior to this renowned clinical trial, two study groups reported that positive responses to the PP were better predicted by changes in \({\text{PaCO}}_{2}\) rather than \({\text{PaO}}_{2} /{\text{FiO}}_{2}\) [41, 42]. However, lately, using \({\text{PaO}}_{2} /{\text{FiO}}_{2}\) to determine PP responders was showed to be a reliable indicator of patients who would survive, especially in the literature that focused on selective patients with COVID-19-ARDS [43,44,45].

Can this contradiction be explained? Enlightened by the findings from Gattinoni et al. [7, 41, 46], the most rational explanation for this contradiction may be as follows: Provided that hemodynamics do not change [47], for a large proportion of COVID-19-ARDS and a lower percentage of classical ARDS patients, an improved \({\text{PaO}}_{2}\) caused by redistributed blood flow indicates success in the PP; in regard to most classical ARDS and remaining COVID-19 ARDS patients, after the PP recruits collapsed or flooded lung units, a fall in \({\text{PaCO}}_{2}\) occurs, albeit in combination with an increased \({\text{PaO}}_{2}\). However, considering the low resistance to diffusion of \({\text{CO}}_{2}\), a change in \({\text{PaCO}}_{2}\) is a more sensitive marker than \({\text{PaO}}_{2}\) [42]. Thus, once a patient exhibits a decreased \({\text{PaCO}}_{2}\), clinicians can identify this patient as a PP responder.

Admittedly, whether \({\text{PaO}}_{2}\) or \({\text{PaCO}}_{2}\) is the best predictor of PP responses is phenotype dependent, and PP responders must correspond to enhanced \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) homogeneity. Furthermore, using EIT, recent studies confirmed that the PP could improve \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) matching not only in patients with COVID-19-ARDS but also in non-COVID-19-ARDS patients [48, 49]. Recently, two study groups used VR to determine PP responders [45, 50]. Overall, dead space ventilation-related parameters may be used to predict positive responses during the PP.

Identifications of candidates for extracorporeal CO2 removal (ECCO2R)

In the recent REST trial, in contrast to lung protective ventilation, ECCO2R-facillitated ultraprotective ventilation did not significantly reduce 90-day mortality, but a higher incidence of complications was observed [51]. Thus, weighing the benefits against adverse events and identifying the best candidates for ECCO2R are issues that remain to be addressed [27, 52,53,54]. Goligher et al. found that an increased \({\text{VD}}_{{{\text{alv}}}} /{\text{VT}}\) could be used to predict a reduced driving pressure and a fall in VT after ECCO2R [55, 56]. The implication of this observation is that for patients with \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch resulting from an elevated \({\text{VD}}_{{{\text{alv}}}} /{\text{VT}}\), using ECCO2R to promote \({\text{CO}}_{2}\) removal would be more beneficial.

Guide to weaning from venovenous extracorporeal membrane oxygenation (vv-ECMO)

The standardized weaning protocol of vv-ECMO for patients with ARDS remains undetermined. Currently, the decision regarding liberation from vv-ECMO is mainly based on oxygenation [57, 58]. However, Al-Fares et al. suggested that VR (i.e., VR > 2.3, sensitivity = 100%, specificity = 81%) could be employed to predict the likelihood of safe liberation from vv-ECMO [59]. More recently, a negative impact of lower baseline \(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\) on weaning outcome was demonstrated by Lazzari and colleagues. The cutoff value of this parameter was 0.84 (sensitivity = 92%, specificity = 80%) [60]. Hence, the optimal time to safely disconnect a patient from vv-ECMO is when the gas exchange in native lungs is improved, which is signified by dead space ventilation-related indices.


Although dead space ventilation-related indices are promising bedside tools to assess \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch, several limitations must be highlighted. In general, these parameters merely indicate overall \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch and therefore are not perfect substitutes for more precise techniques, such as EIT [61].

Physiological dead space fraction

Direct measurements of \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) still rely on volumetric capnography (Vcap), and some technical difficulties limit its widespread use in the clinical setting [62]. Thus, some researchers have already developed several methods to indirectly estimate \({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) without using Vcap (Table 2) [10, 11]. However, the accuracy of these methods is still under debate [11, 42].

Ventilatory ratio

According to the other form of its equation (Table 2), VR is influenced by \({\dot{\text{V}}\text{CO}}_{2}\) [17,18,19, 21]. In the early 1990s, authors found that \({\dot{\text{V}}\text{CO}}_{2}\) was a less influential contributor to excess \({\dot{\text{V}}\text{E}}\) compared with dead space in early ARDS [63]; nevertheless, in the era when ECLS is increasingly prevalent, changes in \({\dot{\text{V}}\text{CO}}_{2}\) can be encountered in ECLS-treated patients. Hence, VR is a parameter of great value to patients receiving ECLS; moreover, when making interpatient comparisons, alterations in \({\dot{\text{V}}\text{CO}}_{2}\) caused by ECLS should also be considered. This could account for the results obtained from two recent studies: (a) Morales-Quinteros et al. found that VR cannot be used as an indicator of mortality [25] and (b) Langer et al. employed VR to predict PP responders; however, this attempt failed as well [45].

End-tidal-to-arterial PCO2 ratio

Limitations of this index include the following: (a) The premise of this index is that \({\text{PETCO}}_{2}\) serves as a surrogate for \({\text{PACO}}_{2}\), In ARDS lungs, because the units with different \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) values empty sequentially, \({\text{PETCO}}_{2}\) is greater than \({\text{PACO}}_{2}\) [16]. (b) This parameter does not take \({\text{VD}}_{{{\text{aw}}}}\) into account. Previous studies have shown that in mechanically ventilated patients, \({\text{VD}}_{{{\text{inst}}}}\) which is an unfixed component of \({\text{VD}}_{{{\text{aw}}}}\) heavily influences ventilatory efficiency [64]. (c) Variations in \({\dot{\text{V}}\text{CO}}_{2}\) and \({\dot{\text{V}}\text{E}}\) receive no attention in this index. For patients treated with ECLS, different extracorporeal blood flow values would induce disparities in \({\dot{\text{V}}\text{CO}}_{2}\). Additionally, \({\dot{\text{V}}\text{E}}\) would rise in proportion to increased \({\dot{\text{V}}\text{CO}}_{2}\).

Future outlook

Optimization of the subclassifications of ARDS

Although \({\dot{\text{V}}\text{E}}_{{{\text{CORR}}}}\) failed to identify a subgroup of patients with more dismal outcomes, emerging clinical studies have revealed that a group of dead space ventilation-related indices can provide prognostic information for patients with ARDS [2, 17,18,19,20,21,22, 24, 25]. Furthermore, adding these indices to the Berlin definition has been demonstrated to improve predictive validity [11, 21]. If their prognostic value could later be confirmed in large-scale randomized controlled trials, dead space ventilation-related indices may be reconsidered when experts update the definition of ARDS to optimize subclassifications in the future.

Identifications of ARDS subphenotypes to achieve precision medicine

Before the outbreak of COVID-19, to enhance personalized therapy, several approaches for identifying subphenotypes were proposed [65]. After Gattinoni et al. recommended that COVID-19-ARDS be divided into phenotype L (i.e., high Crs) and phenotype H (i.e., low Crs) [7, 46], one study group found that this atypical subphenotype with preserved Crs existed in non-COVID-19-ARDS [66]. Recently, Wendel Garcia et al.identified two subphenotypes characterized by different \({\text{VD}}_{{{\text{alv}}}} /{\text{VT}}\) ratios that responded differently to standardized recruitment maneuvers and had disparate clinical outcomes [67]. Therefore, identifying subphenotypes based on these dead space ventilation-related indices makes it possible for the treatment strategies of ARDS to move from a one-size-fits-all pattern toward a more effective and individualized pattern.


Over the past decades, since the significance of dead space was emphasized, a large number of innovative dead space ventilation-related indices have emerged. These parameters inform intensivists about \({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) mismatch, thus assuming a pivotal role in PEEP titration, PP andECLS. With the advent of precision medicine, the management of ARDS is rapidly changing, and dead space ventilation-related indices will return to the forefront of research and clinical practice.

Availability of data and materials

Not applicable.



Acute respiratory distress syndrome


Coronavirus disease 2019


Positive end expiratory pressure


Prone position


Extracorporeal life support

\({\text{PaO}}_{2} /{\text{FiO}}_{2}\) :

Arterial partial pressure of \({\text{O}}_{2}\) to fraction of inspired \({\text{O}}_{2}\)

\({\dot{\text{V}}\text{E}}_{{{\text{CORR}}}}\) :

The corrected minute ventilation

\({\text{VD}}_{{{\text{Bohr}}}} /{\text{VT}}\) :

Bohr’s dead space fraction

\({\text{VD}}_{{{\text{aw}}}} /{\text{VT}}\) :

Airway dead space fraction

\({\text{VD}}_{{{\text{B}} - {\text{E}}}} /{\text{VT}}\) :

Enghoff’s modification of Bohr’s dead space fraction

\({\text{VD}}_{{{\text{phys}}}} /{\text{VT}}\) :

Physiological dead space fraction

\({\dot{\text{Q}}}_{{{\text{VA}}}} /{\dot{\text{Q}}}_{{\text{T}}}\) :

Venous admixture

\({\dot{\text{V}}\text{A}}/{\dot{\text{Q}}}\) :

The ratio of ventilation to perfusion


Ventilatory ratio

\(\frac{{{\text{VD}}_{{{\text{alv}}}} }}{{{\text{VT}}_{{{\text{alv}}}} }}\) :

The ratio of alveolar dead space to alveolar tidal volume

\(\frac{{{\text{PETCO}}_{2} }}{{{\text{PaCO}}_{2} }}\) :

End-tidal-to-arterial PCO2 ratio


Multiple inert gas elimination technique


Automatic lung parameter estimator


Electrical impedance tomography

\({\text{P}}\left( {{\text{a}} - {\text{ET}}} \right){\text{CO}}_{2}\) :

Arterial minus end-tidal CO2 gradient


Extracorporeal CO2 removal

\({\text{VD}}_{{{\text{alv}}}} /{\text{VT}}\) :

Alveolar dead space fraction


Venovenous extracorporeal membrane oxygenation


Volumetric capnography

\({\dot{\text{V}}\text{CO}}_{2}\) :

CO2 production

\({\dot{\text{V}}\text{E}}\) :

Minute ventilation


Compliance of respiratory system


  1. Gorman EA, O’Kane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022;400(10358):1157–70.

    Article  Google Scholar 

  2. Nuckton TJ, Alonso JA, Kallet RH, Daniel BM, Pittet JF, Eisner MD, Matthay MA. Pulmonary dead-space fraction as a risk factor for death in the acute respiratory distress syndrome. N Engl J Med. 2002;346(17):1281–6.

    Article  Google Scholar 

  3. ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307(23):2526–33.

    Google Scholar 

  4. Hernu R, Wallet F, Thiollière F, Martin O, Richard JC, Schmitt Z, et al. An attempt to validate the modification of the American-European consensus definition of acute lung injury/acute respiratory distress syndrome by the Berlin definition in a university hospital. Intensive Care Med. 2013;39(12):2161–70.

    Article  CAS  Google Scholar 

  5. Liu X, Liu X, Xu Y, Xu Z, Huang Y, Chen S, et al. Ventilatory ratio in hypercapnic mechanically ventilated patients with COVID-19-associated acute respiratory distress syndrome. Am J Respir Crit Care Med. 2020;201(10):1297–9.

    Article  CAS  Google Scholar 

  6. Sinha P, Fauvel NJ, Singh S, Soni N. Ventilatory ratio: a simple bedside measure of ventilation. Br J Anaesth. 2009;102(5):692–7.

    Article  CAS  Google Scholar 

  7. Gattinoni L, Chiumello D, Rossi S. COVID-19 pneumonia: ARDS or not? Crit Care. 2020;24(1):154.

    Article  Google Scholar 

  8. Bohr C. Ueber die Lungenathmung. Skand Arch Physiol. 1891;2:236–8.

    Article  Google Scholar 

  9. Englhoff H. Volumen inefficax. Bemerkungen zur frage des schädlichen raumes. Uppsala Läkareforen Forhandl. 1938;44:191–218.

    Google Scholar 

  10. Siddiki H, Kojicic M, Li G, Yilmaz M, Thompson TB, Hubmayr RD, Gajic O. Bedside quantification of dead-space fraction using routine clinical data in patients with acute lung injury: secondary analysis of two prospective trials. Crit Care. 2010;14(4):R141.

    Article  Google Scholar 

  11. Beitler JR, Thompson BT, Matthay MA, Talmor D, Liu KD, Zhuo H, et al. Estimating dead-space fraction for secondary analyses of acute respiratory distress syndrome clinical trials. Crit Care Med. 2015;43(5):1026–35.

    Article  CAS  Google Scholar 

  12. Robertson HT. Dead space: the physiology of wasted ventilation. Eur Respir J. 2015;45(6):1704–16.

    Article  Google Scholar 

  13. Verscheure S, Massion PB, Verschuren F, Damas P, Magder S. Volumetric capnography: lessons from the past and current clinical applications. Crit Care. 2016;20(1):184.

    Article  Google Scholar 

  14. Riley RL, Cournand A. Ideal alveolar air and the analysis of ventilation-perfusion relationships in the lungs. J Appl Physiol. 1949;1(12):825–47.

    Article  CAS  Google Scholar 

  15. Radermacher P, Maggiore SM, Mercat A. Fifty years of research in ARDS. Gas exchange in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2017;196(8):964–84.

    Article  CAS  Google Scholar 

  16. Petersson J, Glenny RW. Gas exchange and ventilation-perfusion relationships in the lung. Eur Respir J. 2014;44(4):1023–41.

    Article  Google Scholar 

  17. Sinha P, Fauvel NJ, Singh P, Soni N. Analysis of ventilatory ratio as a novel method to monitor ventilatory adequacy at the bedside. Crit Care. 2013;17(1):R34.

    Article  Google Scholar 

  18. Sinha P, Sanders RD, Soni N, Vukoja MK, Gajic O. Acute respiratory distress syndrome: the prognostic value of ventilatory ratio–a simple bedside tool to monitor ventilatory efficiency. Am J Respir Crit Care Med. 2013;187(10):1150–3.

    Article  Google Scholar 

  19. Sinha P, Singh S, Hardman JG, Bersten AD, Soni N, Australia and New Zealand Intensive Care Society Clinical Trials Group. Evaluation of the physiological properties of ventilatory ratio in a computational cardiopulmonary model and its clinical application in an acute respiratory distress syndrome population. Br J Anaesth. 2014;112(1):96–101.

    Article  CAS  Google Scholar 

  20. Morales-Quinteros L, Schultz MJ, Bringué J, Calfee CS, Camprubí M, Cremer OL, MARS Consortium, et al. Estimated dead space fraction and the ventilatory ratio are associated with mortality in early ARDS. Ann Intensive Care. 2019;9(1):128.

    Article  Google Scholar 

  21. Sinha P, Calfee CS, Beitler JR, Soni N, Ho K, Matthay MA, Kallet RH. Physiologic analysis and clinical performance of the ventilatory ratio in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2019;199(3):333–41.

    Article  Google Scholar 

  22. Torres A, Motos A, Riera J, Fernández-Barat L, Ceccato A, Pérez-Arnal R, CIBERESUCICOVID Project (COV20/00110, ISCIII), et al. The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients. Crit Care. 2021;25(1):331.

    Article  Google Scholar 

  23. Anderson CT, Breen PH. Carbon dioxide kinetics and capnography during critical care. Crit Care. 2000;4(4):207–15.

    Article  CAS  Google Scholar 

  24. Kallet RH, Lipnick MS. End-tidal-to-arterial PCO2 ratio as signifier for physiologic dead-space ratio and oxygenation dysfunction in acute respiratory distress syndrome. Respir Care. 2021;66(2):263–8.

    Article  Google Scholar 

  25. Morales-Quinteros L, Neto AS, Artigas A, Blanch L, Botta M, Kaufman DA, PRoVENT-COVID Study Group, et al. Dead space estimates may not be independently associated with 28-day mortality in COVID-19 ARDS. Crit Care. 2021;25(1):171.

    Article  Google Scholar 

  26. Chiumello D, Busana M, Coppola S, Romitti F, Formenti P, Bonifazi M, et al. Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study. Intensive Care Med. 2020;46(12):2187–96.

    Article  CAS  Google Scholar 

  27. Gendreau S, Geri G, Pham T, Vieillard-Baron A, Mekontso DA. The role of acute hypercapnia on mortality and short-term physiology in patients mechanically ventilated for ARDS: a systematic review and meta-analysis. Intensive Care Med. 2022;48(5):517–34.

    Article  CAS  Google Scholar 

  28. Wagner PD. The multiple inert gas elimination technique (MIGET). Intensive Care Med. 2008;34(6):994–1001.

    Article  Google Scholar 

  29. Karbing DS, Panigada M, Bottino N, Spinelli E, Protti A, Rees SE, Gattinoni L. Changes in shunt, ventilation/perfusion mismatch, and lung aeration with PEEP in patients with ARDS: a prospective single-arm interventional study. Crit Care. 2020;24(1):111.

    Article  Google Scholar 

  30. Spinelli E, Kircher M, Stender B, Ottaviani I, Basile MC, Marongiu I, et al. Unmatched ventilation and perfusion measured by electrical impedance tomography predicts the outcome of ARDS. Crit Care. 2021;25(1):192.

    Article  Google Scholar 

  31. Suter PM, Fairley B, Isenberg MD. Optimum end-expiratory airway pressure in patients with acute pulmonary failure. N Engl J Med. 1975;292(6):284–9.

    Article  CAS  Google Scholar 

  32. Murray IP, Modell JH, Gallagher TJ, Banner MJ. Titration of PEEP by the arterial minus end-tidal carbon dioxide gradient. Chest. 1984;85(1):100–4.

    Article  CAS  Google Scholar 

  33. Blanch L, Fernández R, Benito S, Mancebo J, Net A. Effect of PEEP on the arterial minus end-tidal carbon dioxide gradient. Chest. 1987;92(3):451–4.

    Article  CAS  Google Scholar 

  34. Tusman G, Suarez-Sipmann F, Böhm SH, Pech T, Reissmann H, Meschino G, Scandurra A, Hedenstierna G. Monitoring dead space during recruitment and PEEP titration in an experimental model. Intensive Care Med. 2006;32(11):1863–71.

    Article  Google Scholar 

  35. Tusman G, Suarez-Sipmann F, Bohm SH, Borges JB, Hedenstierna G. Capnography reflects ventilation/perfusion distribution in a model of acute lung injury. Acta Anaesthesiol Scand. 2011;55(5):597–606.

    Article  CAS  Google Scholar 

  36. Yang Y, Huang Y, Tang R, Chen Q, Hui X, Li Y, et al. Optimization of positive end-expiratory pressure by volumetric capnography variables in lavage-induced acute lung injury. Respiration. 2014;87(1):75–83.

    Article  Google Scholar 

  37. Guérin C, Albert RK, Beitler J, Gattinoni L, Jaber S, Marini JJ, et al. Prone position in ARDS patients: why, when, how and for whom. Intensive Care Med. 2020;46(12):2385–96.

    Article  Google Scholar 

  38. Langer M, Mascheroni D, Marcolin R, Gattinoni L. The prone position in ARDS patients. Clin Study Chest. 1988;94(1):103–7.

    CAS  Google Scholar 

  39. Guérin C, Reignier J, Richard JC, Beuret P, Gacouin A, Boulain T, PROSEVA Study Group, et al. Prone positioning in severe acute respiratory distress syndrome. N Engl J Med. 2013;368(23):2159–68.

    Article  Google Scholar 

  40. Albert RK, Keniston A, Baboi L, Ayzac L, Guérin C, Proseva Investigators. Prone position-induced improvement in gas exchange does not predict improved survival in the acute respiratory distress syndrome. Am J Respir Crit Care Med. 2014;189(4):494–6.

    Article  Google Scholar 

  41. Gattinoni L, Vagginelli F, Carlesso E, Taccone P, Conte V, Chiumello D, Prone-Supine Study Group, et al. Decrease in PaCO2 with prone position is predictive of improved outcome in acute respiratory distress syndrome. Crit Care Med. 2003;31(12):2727–33.

    Article  Google Scholar 

  42. Charron C, Repesse X, Bouferrache K, Bodson L, Castro S, Page B, et al. PaCO2 and alveolar dead space are more relevant than PaO2/FiO2 ratio in monitoring the respiratory response to prone position in ARDS patients: a physiological study. Crit Care. 2011;15(4):R175.

    Article  Google Scholar 

  43. Lee HY, Cho J, Kwak N, Choi SM, Lee J, Park YS, et al. Improved oxygenation after prone positioning may be a predictor of survival in patients with acute respiratory distress syndrome. Crit Care Med. 2020;48(12):1729–36.

    Article  CAS  Google Scholar 

  44. Scaramuzzo G, Gamberini L, Tonetti T, Zani G, Ottaviani I, Mazzoli CA, ICU-RER COVID-19 Collaboration, et al. Sustained oxygenation improvement after first prone positioning is associated with liberation from mechanical ventilation and mortality in critically ill COVID-19 patients: a cohort study. Ann Intensive Care. 2021;11(1):63.

    Article  CAS  Google Scholar 

  45. Langer T, Brioni M, Guzzardella A, Carlesso E, Cabrini L, Castelli G, PRONA-COVID Group, et al. Prone position in intubated, mechanically ventilated patients with COVID-19: a multi-centric study of more than 1000 patients. Crit Care. 2021;25(1):128.

    Article  Google Scholar 

  46. Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, Camporota L. COVID-19 pneumonia: Different respiratory treatments for different phenotypes? Intensive Care Med. 2020;46(6):1099–102.

    Article  CAS  Google Scholar 

  47. Jozwiak M, Teboul JL, Anguel N, Persichini R, Silva S, Chemla D, et al. Beneficial hemodynamic effects of prone positioning in patients with acute respiratory distress syndrome. Am J Respir Crit Care Med. 2013;188(12):1428–33.

    Article  Google Scholar 

  48. Perier F, Tuffet S, Maraffi T, Alcala G, Victor M, Haudebourg AF, et al. Effect of positive end-expiratory pressure and proning on ventilation and perfusion in COVID-19 acute respiratory distress syndrome. Am J Respir Crit Care Med. 2020;202(12):1713–7.

    Article  CAS  Google Scholar 

  49. Wang YX, Zhong M, Dong MH, Song JQ, Zheng YJ, Wu W, et al. Prone positioning improves ventilation-perfusion matching assessed by electrical impedance tomography in patients with ARDS: a prospective physiological study. Crit Care. 2022;26(1):154.

    Article  Google Scholar 

  50. Dam TA, Roggeveen LF, van Diggelen F, Fleuren LM, Jagesar AR, Otten M, Dutch ICU Data Sharing Against COVID-19 Collaborators, et al. Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning. Ann Intensive Care. 2022;12(1):99.

    Article  CAS  Google Scholar 

  51. McNamee JJ, Gillies MA, Barrett NA, Perkins GD, Tunnicliffe W, Young D, et al. Effect of lower tidal volume ventilation facilitated by extracorporeal carbon dioxide removal vs standard care ventilation on 90-day mortality in patients with acute hypoxemic respiratory failure: the REST randomized clinical trial. JAMA. 2021;326(11):1013–23.

    Article  CAS  Google Scholar 

  52. Combes A, Fanelli V, Pham T, Ranieri VM, European Society of Intensive Care Medicine Trials Group and the “Strategy of Ultra-Protective lung ventilation with Extracorporeal CO2 Removal for New-Onset moderate to severe ARDS” (SUPERNOVA) investigators. Feasibility and safety of extracorporeal CO2 removal to enhance protective ventilation in acute respiratory distress syndrome: the SUPERNOVA study. Intensive Care Med. 2019;45(5):592–600.

    Article  Google Scholar 

  53. Giraud R, Banfi C, Assouline B, De Charrière A, Cecconi M, Bendjelid K. The use of extracorporeal CO2 removal in acute respiratory failure. Ann Intensive Care. 2021;11(1):43.

    Article  CAS  Google Scholar 

  54. Gattinoni L, Coppola S, Camporota L. Physiology of extracorporeal CO2 removal. Intensive Care Med. 2022;48(10):1322–5.

    Article  CAS  Google Scholar 

  55. Goligher EC, Amato MBP, Slutsky AS. Applying precision medicine to trial design using physiology. Extracorporeal CO2 removal for acute respiratory distress syndrome. Am J Respir Crit Care Med. 2017;196(5):558–68.

    Article  Google Scholar 

  56. Goligher EC, Combes A, Brodie D, Ferguson ND, Pesenti AM, Ranieri VM, Slutsky AS, SUPERNOVA investigators (European Society of Intensive Care Medicine trials group) and for the International ECMO Network (ECMONet). Determinants of the effect of extracorporeal carbon dioxide removal in the SUPERNOVA trial: implications for trial design. Intensive Care Med. 2019;45(9):1219–30.

    Article  CAS  Google Scholar 

  57. Gannon WD, Stokes JW, Bloom S, Sherrill W, Bacchetta M, Rice TW, et al. Safety and feasibility of a protocolized daily assessment of readiness for liberation from venovenous extracorporeal membrane oxygenation. Chest. 2021;160(5):1693–703.

    Article  CAS  Google Scholar 

  58. Tonna JE, Abrams D, Brodie D, Greenwood JC, Rubio Mateo-Sidron JA, Usman A, Fan E. Management of adult patients supported with venovenous extracorporeal membrane oxygenation (VV ECMO): guideline from the extracorporeal life support organization (ELSO). ASAIO J. 2021;67(6):601–10.

    Article  CAS  Google Scholar 

  59. Al-Fares AA, Ferguson ND, Ma J, Cypel M, Keshavjee S, Fan E, Del Sorbo L. Achieving safe liberation during weaning from VV-ECMO in patients with severe ARDS: the role of tidal volume and inspiratory effort. Chest. 2021;160(5):1704–13.

    Article  Google Scholar 

  60. Lazzari S, Romitti F, Busana M, Vassalli F, Bonifazi M, Macrí MM, et al. End-tidal to arterial PCO2 ratio as guide to weaning from venovenous extracorporeal membrane oxygenation. Am J Respir Crit Care Med. 2022;206(8):973–80.

    Article  Google Scholar 

  61. Bitker L, Talmor D, Richard JC. Imaging the acute respiratory distress syndrome: past, present and future. Intensive Care Med. 2022;48(8):995–1008.

    Article  Google Scholar 

  62. Suárez-Sipmann F, Villar J, Ferrando C, Sánchez-Giralt JA, Tusman G. Monitoring expired CO2 kinetics to individualize lung-protective ventilation in patients with the acute respiratory distress syndrome. Front Physiol. 2021;12: 785014.

    Article  Google Scholar 

  63. Ravenscraft SA, McArthur CD, Path MJ, Iber C. Components of excess ventilation in patients initiated on mechanical ventilation. Crit Care Med. 1991;19(7):916–25.

    Article  CAS  Google Scholar 

  64. Morán I, Bellapart J, Vari A, Mancebo J. Heat and moisture exchangers and heated humidifiers in acute lung injury/acute respiratory distress syndrome patients. Effects on respiratory mechanics and gas exchange. Intensive Care Med. 2006;32(4):524–31.

    Article  Google Scholar 

  65. Wilson JG, Calfee CS. ARDS subphenotypes: understanding a heterogeneous syndrome. Crit Care. 2020;24(1):102.

    Article  Google Scholar 

  66. Panwar R, Madotto F, Laffey JG, van Haren FMP. Compliance phenotypes in early acute respiratory distress syndrome before the COVID-19 pandemic. Am J Respir Crit Care Med. 2020;202(9):1244–52.

    Article  CAS  Google Scholar 

  67. Wendel Garcia PD, Caccioppola A, Coppola S, Pozzi T, Ciabattoni A, Cenci S, Chiumello D. Latent class analysis to predict intensive care outcomes in acute respiratory distress syndrome: a proposal of two pulmonary phenotypes. Crit Care. 2021;25(1):154.

    Article  Google Scholar 

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Zheng, M. Dead space ventilation-related indices: bedside tools to evaluate the ventilation and perfusion relationship in patients with acute respiratory distress syndrome. Crit Care 27, 46 (2023).

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