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.
Dead Space Liberation Pdf
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 \(\textPaO_2 /\textFiO_2\)(i.e., arterial partial pressure of \(\textO_2\) to fraction of inspired \(\textO_2\)) 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], \(\dot\textV\textE_\textCORR\) (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 \(\dot\textV\textE_\textCORR\) [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.
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.
It is widely accepted that the presence of true dead space units (i.e., \(\dot\textV\textA/\dot\textQ = \infty\)) and high \(\dot\textV\textA/\dot\textQ\) units could cause hypercapnia, and an increase in \(\dot\textV\textE\) could facilitate \(\textCO_2\) elimination to maintain an unchanged \(\textPaCO_2\), which implies an association between \(\dot\textV\textE\) and \(\textPaCO_2\). Therefore, the ventilatory ratio (VR) was developed to better evaluate ventilatory efficiency. VR is described as \(\textVR = \frac\dot\textV\textE_\textmeasured \times \textPaCO_2\textmeasured \dot\textV\textE_\textpredicted \times \textPaCO_2\textpredicted \) (Table 2). Likewise, VR reflects a continuous spectrum of \(\dot\textV\textA/\dot\textQ\) mismatch in the lung [6]. Not surprisingly, authors have also validated that there is an intimate correlation between VR and \(\textVD_\textphys /\textVT\) [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].
In the last century, authors calculated the ratio of alveolar dead space to alveolar tidal volume (i.e., \(\frac\textVD_\textalv \textVT_\textalv \)), which equals \(\frac\textP\left( \texta - \textET \right)\textCO_2 \textPaCO_2 \) and can be restated as \(1 - \textPETCO_2 /\textPaCO_2\) [23]. Recently, Gattinoni et al. suggested that the end-tidal-to-arterial PCO2 ratio \(\left( \frac\textPETCO_2 \textPaCO_2 \right)\) (Table 2) deriving from \(\frac\textVD_\textalv \textVT_\textalv \) could be used as a bedside tool to monitor gas exchanges of patients with COVID-19-ARDS. \(\frac\textPETCO_2 \textPaCO_2 \) is also a global index with which to assess \(\dot\textV\textA/\dot\textQ\) 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 \(\frac\textPETCO_2 \textPaCO_2 \) and \(\textVD_\textphys /\textVT\); in addition, a reduction in \(\frac\textPETCO_2 \textPaCO_2 \) was associated with a higher mortality risk in the non-COVID-19-ARDS and COVID-19-ARDS populations [24, 25].
Almost 50 years ago, Suter et al. first defined the optimal PEEP as that giving rise to the lowest \(\textVD_\textphys /\textVT\) [31]. Furthermore, other studies demonstrated that indices such as the arterial minus end-tidal \(\textCO_2\) gradient (i.e., \(\textP\left( \texta - \textET \right)\textCO_2\)) and the ratio of alveolar dead space to alveolar tidal volume (i.e., \(\frac\textVD_\textalv \textVT_\textalv \)) were also helpful in PEEP titration [32,33,34,35,36]. These two indices are analogous to \(\frac\textPETCO_2 \textPaCO_2 \); thus, both are associated with dead space, or more specifically \(\dot\textV\textA/\dot\textQ\) matching. Altogether, employing these dead space ventilation-related parameters may help to titrate the optimal PEEP.
Admittedly, whether \(\textPaO_2\) or \(\textPaCO_2\) is the best predictor of PP responses is phenotype dependent, and PP responders must correspond to enhanced \(\dot\textV\textA/\dot\textQ\) homogeneity. Furthermore, using EIT, recent studies confirmed that the PP could improve \(\dot\textV\textA/\dot\textQ\) 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.
Although dead space ventilation-related indices are promising bedside tools to assess \(\dot\textV\textA/\dot\textQ\) mismatch, several limitations must be highlighted. In general, these parameters merely indicate overall \(\dot\textV\textA/\dot\textQ\) mismatch and therefore are not perfect substitutes for more precise techniques, such as EIT [61].
According to the other form of its equation (Table 2), VR is influenced by \(\dot\textV\textCO_2\) [17,18,19, 21]. In the early 1990s, authors found that \(\dot\textV\textCO_2\) was a less influential contributor to excess \(\dot\textV\textE\) compared with dead space in early ARDS [63]; nevertheless, in the era when ECLS is increasingly prevalent, changes in \(\dot\textV\textCO_2\) 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 \(\dot\textV\textCO_2\) 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].
Although \(\dot\textV\textE_\textCORR\) 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.
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 \(\textVD_\textalv /\textVT\) 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 \(\dot\textV\textA/\dot\textQ\) 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. 2ff7e9595c
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