Background
Since the outbreak of coronavirus disease 2019 (COVID-19) in the City of Wuhan, Hubei Province, China, caused by the transmission of the novel coronavirus SARS-CoV-2, millions of individuals have been infected and more than one million have died. Severe disease requiring admission to intensive care unit (ICU) occurs in approximately 5% of infections [
1], and the most common reason for admission is respiratory failure requiring high-level support. Among these patients, two-thirds meet the criteria for the acute respiratory distress syndrome (ARDS) [
2].
Patients with COVID-19 pneumonia meeting criteria for ARDS usually present with a high respiratory drive and minute ventilation, potentially due to hypercapnia and an increased dead space fraction (
VD/
VT) [
3]. In patients with ARDS, an elevated
VD/
VT is a predictor of death and is one of the few lung-specific physiological variables independently associated with mortality [
4,
5]. Methods for estimating
VD/
VT do not require quantitative assessment of exhaled carbon dioxide, are less complicated to perform and easier to calculate at the bedside compared with calculations made by volumetric capnography [
6]. In recent years, the ventilatory ratio (VR) was proposed as an easily acquired bedside index of impaired ventilation that can be computed using routinely measured respiratory variables [
7]. In patients with ARDS, the VR correlates well with
VD/
VT [
7] and may function as a surrogate marker for impaired ventilation [
8].
At least two independent groups have described series of patients with COVID-19-related ARDS who may have inefficient CO
2 removal due to increased physiologic dead space [
3,
9]. However, few studies have assessed the impact for dead space ventilation mortality in a large cohort of COVID-19 patients undergoing invasive ventilation [
10]. Therefore, we aimed to assess the association between markers of impaired ventilation, such as estimated
VD/
VT and VR with 28-day mortality in patients undergoing invasive ventilation because of COVID-19 ARDS. We hypothesized that these markers of impaired ventilation are independently associated with 28-day mortality.
Methods
Study design and oversight
PRoVENT-COVID is an investigator-initiated, multicenter, observational cohort study undertaken at 22 ICUs in the Netherlands. The study protocol including the statistical analysis plan is available [
11]. The approved protocol is available in Additional File
1. A statistical analysis plan for the current analysis was written before assessing the database and is available online [
12]. Study sites were recruited through direct contact by members of the steering committee of PRoVENT-COVID. The institutional review boards of the participating centers approved the study protocol, and need for patient informed consent was waived. Study coordinators contacted the local doctors, trained data collectors to assist the local doctors and monitored the study according to the International Conference on Harmonization Good Clinical Practice–guidelines. Integrity and timely completion of data collection was ensured by the study coordinators.
Patients
Consecutive patients ≥ 18 years of age were eligible for participation in PRoVENT-COVID if they were admitted to one of the participating ICUs and had received invasive ventilation for COVID-19 ARDS. COVID-19 infection was defined by a confirmed reverse transcriptase-polymerase chain reaction (RT-PCR) [
13].
PRoVENT-COVID had no exclusion criteria, but for the current analysis, we excluded patients transferred from a non-participating hospital who had been receiving invasive ventilation for more than 2 calendar days, patients without complete data to calculate the VD/VT or VR on the first day of ventilation, and patients with no data about 28-day mortality.
Data collection
Demographics and data regarding premorbid diseases and medication were collected at baseline. Ventilator settings and parameters were collected after one hour of invasive ventilation and every 8 h thereafter, for the first 4 calendar days. In the present study, the first day of ventilation is called ‘at start of ventilation.’
Data definition and exposure
The primary exposure of interest was the V
D/V
T calculated using the Harris–Benedict formula as described in Eq. (
1) [
14]:
$$\frac{{V_{{\text{D}}} }}{{V_{{\text{T}}} }} = 1 - \frac{{\left( {0.863* \dot{V}{\text{CO}}_{2} } \right)}}{{\left( {{\text{RR}}* V_{{\text{T}}} * {\text{PaCO}}_{2} } \right)}}$$
(1)
RR is the respiratory rate in breaths per minute, V
T the tidal volume in liters, PaCO
2 the partial pressure of carbon dioxide in mmHg, and VCO
2 the CO
2 production in mL/min estimated using Eq. (
2):
$$\dot{V}{\text{CO}}_{2} = \frac{{{\text{REE}}_{{{\text{HB}}}} }}{{\left( {\frac{5.616}{{{\text{RQ}}}} + 1.584} \right)}}$$
(2)
RQ is the respiratory quotient, assumed to be 0.8, and REE
HB is the rest energy expenditure calculated by the unadjusted Harris–Benedict estimate using Eq. (
3) [
14]:
$$\begin{aligned} {\text{Males}}:{\text{REE}}_{{{\text{HB}}}} & = 66.473 + \left( {13.752*{\text{weight}}} \right) + \left( {5.003*{\text{height}}} \right) - \left( {6.755*{\text{age}}} \right) \\ {\text{Females}}:{\text{REE}}_{{{\text{HB}}}} & = 655.096 + \left( {9.563*{\text{weight}}} \right) + \left( {1.850*{\text{height}}} \right) - \left( {4.676*{\text{age}}} \right) \\ \end{aligned}$$
(3)
Weight is the actual body weight in kilograms, height is in centimeters and age in years.
In addition, two additional estimations of V
D/V
T were done considering a direct estimation [
6] and the end-tidal-to-arterial PCO
2 ratio [
15], and the formulas are described in the Additional File
1.
The secondary exposure of interest is the VR, calculated using Eq. (
4) [
16]:
$${\text{VR}} = \frac{{\dot{V}_{{E {\text{measured}}}} * {\text{PaCO}}_{{2 {\text{measured}}}} }}{{\dot{V}_{{E {\text{predicted}}}} * {\text{PaCO}}_{{2 {\text{predicted}}}} }}$$
(4)
VR is the ventilatory ratio,
VE measured is the measured minute ventilation in mL/min, PaCO
2 measured is the measured PaCO
2 in mmHg,
VE predicted is the predicted minute ventilation in mL/min (calculated as 100 * predicted body weight) [
16], and PaCO
2 predicted is the predicted PaCO
2 determined as 37.5 mmHg.
A post-hoc analysis was performed using Corrected Minute Ventilation as additional parameter of wasted ventilation. This parameter is calculated using the following formula:
$$\dot{V}_{{{\text{E}}\,{\text{corr}}}} = \frac{{\dot{V}_{E } * {\text{PaCO}}_{2 } }}{{40\, {\text{mmHg}}}}$$
(5)
where 40 mm Hg is the ideal value of PaCO
2 [
17]. This is reported in the Additional File
1. Additionally, the delta values between days were calculated and used as additional parameters and reported in the Additional File
1.
All variables were calculated three times per day, and the values were aggregated as the mean in the respective day. Primary analyses focused on the values obtained on the day on which ventilation was started.
Outcomes
The outcome assessed in this study was death at 28 days, defined as the mortality 28 days after the start of ventilation. Other clinical outcomes are reported only to describe the cohort but were not used to test their association with the exposures described above.
Statistical analysis
The amount of missing data was low for most of the variables (Table S1 in Additional File
1). Continuous variables are presented as median (quartile 25%–quartile 75%) and categorical variables as counts and percentages. Descriptive data are presented according to the 28-day status (non-survivors vs. survivors), and the two groups were compared using Wilcoxon rank-sum test for continuous variables, and Fisher exact tests for categorical variables.
Trends in markers of impaired ventilation were presented in boxplots between survivors and non-survivors over the first 4 calendar days. The direction of effect over time of the variables was assessed with mixed–effect linear models with center and patients treated as random effect to account for clustering and repeated measurements, and with 28-day vital status (alive/dead), time (as a continuous variable) and an interaction of 28-day vital status and time as fixed effect. Overall P values from this analysis represent the overall difference among groups over time, and P values from interaction represent a statistical assessment of whether the trend over time differed among the groups. All daily measurements of variables (three times a day) were aggregated as the mean per day. In addition, to compare variables at each day, the day variable was entered as a categorical variable in the model described above, and the P value for the daily difference was extracted using pairwise comparisons after Bonferroni correction.
We examined the risk of death for each tertile of the lung-specific physiological variables used to evaluate whether the predictive ability of each variable varied by level. In addition, a simple stratification creating two groups according to the median of the variables was also assessed. The comparison of the two groups was presented in Kaplan–Meier curves and compared using Log-rank tests.
Univariable mixed-effect generalized linear models considering a binomial distribution and with center as random effect were used to estimate the unadjusted effect of each variable on 28-day mortality. A multivariable mixed-effect generalized linear model considering a binomial distribution and with center as random effect were used to test the association of each of the exposures described above with 28-day mortality. A list of candidate confounders was determined a priori, and based on clinical relevance rather than statistical significance. The following baseline variables (measured at baseline or within 1 h after intubation or ICU admission with ventilation) were considered in the models: age, gender, body mass index, PaO2/FiO2 ratio, plasma creatinine, hypertension, diabetes, use of angiotensin converting enzyme inhibitors, use of angiotensin II receptor blockers, use of a vasopressor or an inotrope drug, fluid balance, pH, mean arterial pressure, heart rate, respiratory system compliance and PEEP. Multicollinearity was assessed through the analysis of the variance inflation factors, and the final model was assessed for discrimination using c-statistics, and for calibration using the Brier-Score.
In addition to the odds ratio (OR) and its 95% confidence interval, the predictive accuracy of the lung-specific physiological variables was measured by the area under the receiver operating characteristics curve (AUC-ROC). Also, to estimate whether these variables improved predictive accuracy on top of that of the base model described above, the net reclassification improvement (NRI) and the integrated discrimination index (IDI) were assessed.
For the primary analysis, covariates with less than 3% of missing values were imputed by the median value of the overall cohort. Since respiratory compliance was missing in 8.2% of the patients (Table S1 in Additional File
1), an additional sensitivity analysis considering multiple imputation for all missing variables was conducted (described in details in Additional File
1).
All continuous variables were entered after standardization to improve convergence of the models, and the odds ratio (OR) represents the increase in one standard deviation of the variable. All analyses were conducted in R v.4.0.2 (R Foundation, Vienna, Austria) [
18], and significance level was set at 0.05.
Discussion
The findings of this multicenter, observational cohort study of COVID-19-related ARDS patients showed that estimates for dead space ventilation increased over the first days of invasive ventilation and were significantly higher in non-survivors than survivors. However, none of these indices was independently associated with mortality when corrected for potential confounders. Therefore, wasted ventilation, and, tentatively, increased estimated dead space fraction, may be a marker for disease severity rather than an independent predictor of outcome.
Despite [
19] the potential clinical value,
VD/
VT is not routinely measured in daily critical care practice. One possible barrier is the requirement of volumetric capnography (or other techniques of analyzing exhaled gas) to measure
Vd/
Vt. Estimated measures for calculating
VD/
VT are more frequently utilized and a wide array of these indices were included in this study [
6,
20]. VR is a recently validated index in patients under controlled modes of mechanical ventilation. This index was shown to be high in patients with COVID-19-related ARDS [
3,
9] and is known to show moderate correlation with
VD/
VT by volumetric capnography [
7]. We found that the VR was not significantly different between survivors and non survivors at the start of ventilation and on day 1. However, we did find a significant difference in the following days of mechanical ventilation between survivors and non-survivors, not only for the VR but also for the rest of dead space estimates under study when a post-hoc analysis was performed (Table S14). This finding suggests the dynamic changes of these estimates over time are much more important than a single variable at the start of mechanical ventilation, also because this includes the response to optimization of ventilator settings.
Recently, the end-tidal-to-arterial PCO
2 ratio (P
ETCO
2/PaCO
2) has been described as another surrogate for
VD/
VT in ARDS patients [
15]. Each of these estimations has particular limitations, and they should be seen as complementary: if all point in the same direction, this likely reflects increased dead space ventilation. For example, in the presence of increased intrapulmonary shunt (as in ARDS patients), rising PaCO
2 coincides with decreasing P
ETCO2. Both shunt and low cardiac output states are known determinants of
VD/
VT. It is worth noting that that the impact of cardiac output exists only when measuring
VD/
VT the Enghoff modification of Bohr's original formula is used. In the case of shunt, the increase in venous admixture will elevate the PaCO
2 increasing dead space fraction [
21]. This contribution is of special importance when
VD/
VT is high, where physiologic dead space can be contaminated by the large shunt fractions present in any type of ARDS. In low cardiac output states, a decrease in pulmonary blood flow leads to a reduced alveolar CO
2 delivery decreasing P
ECO
2, thereby increasing
VD/
VT [
22]. In both cases, indices for increase in dead space fraction would capture these phenomena and is hard to know each part's relative contribution in practice. Taken together, dead space indices reflect impaired outgassing of CO
2 because of abnormal ventilation-perfusion matching giving a good global index of a lung’s gas exchange efficiency [
23,
24].
Dead space estimations were significantly increased in non-survivors in the first four days of mechanical ventilation compared to survivors. This is line with previous studies in all patients with ARDS (not only those with COVID19), in which dead space (
VD/
VT) was elevated during the first week after start of invasive ventilation [
4,
25]. We also described the association between these indices and outcome that was previously observed in patients with ARDS due to other causes than COVID-19 [
4,
25]. However, in our study the investigated estimates did not add predictive value to a model that included other known predictors for 28-day mortality, with the possible exception of P
ETCO
2/PaCO
2 at the start and at day 1 of ventilation. This contrasts with several studies in ARDS that showed increased dead space ventilation to be a robust and independent predictor of mortality risk [
4,
25‐
27]. Decreasing P
ETCO
2/PaCO
2 was also independently associated with mortality risk in one study [
15]. Yet, our findings are in line with a previous report in which we assessed the added value of markers of impaired ventilation during the first days of mechanical ventilation in non-COVID-19-related ARDS [
8]. Taken together, the data suggest that markers of impaired ventilation reflect disease severity but are not independent predictors of outcome, irrespective of the cause of ARDS.
Although not the primary aim of this study, we observed that patients who were deceased were less frequently put in prone positioning (Table S2). Prone position facilitates shape matching, which helps minimizing injurious ventilation and frequently improves gas exchange through better V/Q matching resulting in less shunt and improved CO2 clearance. Therefore, it could be postulated that prone positioning confounds the relation between surrogates of dead space ventilation and outcome. A post-hoc analysis, however, did not show a stronger association between these surrogates and outcomes in patients who did not receive prone position, yielding this explanation less likely.
In the current study in patients with COVID-19-related ARDS, impaired ventilation was already present in the first days of invasive ventilation. The studied estimations for
VD/
VT further increased during the first days of invasive ventilation, especially in patients who did not survive. Altered hemostasis and thrombosis are postulated to be a key element of ARDS, with the endothelium playing a key role by promoting microthrombogenesis [
28‐
30]. Endothelial infection and activation and disorders of the microvasculature have been described in the pathogenesis of COVID-19 [
19], and perfusion defects in the pulmonary arterial circulation are frequently observed [
31]. Autopsy findings include pulmonary vascular microthrombi [
32] in addition to diffuse alveolar damage. These findings could lead to high dead space fraction.
The strengths of this study include the size of the multi-center cohort, careful data collection and with few missing data, the pre-specified analysis plan, and the evaluation of multiple estimations for impaired ventilation. The central limitation of this study is that we did not quantify dead space ventilation directly by volumetric capnography or another technique. This was not possible in the setting of a pandemic, where the critical care systems were overwhelmed with patients. A second limitation is the observational nature of the study. Therefore, this study does not provide insight into potential mechanisms that may contribute to the association between high dead space estimations and mortality in COVID-19-related ARDS patients. Another important aspect to take into account is the aspect of the instrumental dead space. Use of heated humidifiers of HMEs (heat and moisture exchangers) is heterogeneous in the clinical practice, and different HMEs have different dead space volumes. Instrumental dead space may significantly affect the total dead space, mainly when using low tidal volume ventilation, and we have commented on this previously [
33].
For the estimated
VD/
VT computed by the Harris–Benedict formula, we assumed an RQ of 0.8 for VCO
2 calculation based on a previous study [
6]. Although the RQ may vary among ARDS patients, a recently previous work showed a good correlation between the VR (which also depends on the VCO
2) and dead space measured with volumetric capnography [
7].
The results of this study indicate that estimations for increased dead space may not be independently associated with mortality. The observed effect sizes were remarkably similar to those observed in non-COVID-19-related ARDS. This contrasts several reports that have hypothesized that profound endothelial injury and coagulopathy may be central mediators of lung injury in COVID-19 [
34]. We acknowledge that we did not measure these processes in this study, but we do provide evidence that COVID-19-related ARDS appears to be similar to non-COVID ARDS with respect to Vd/Vt. This implies that dead space and its estimates should be understood as a readily available marker of ARDS severity. Whether a high dead space identifies an enriched patient population with a higher prevalence of vascular injury, and who might benefit from treatments aimed at restoring normal pulmonary perfusion is unknown. Previous data suggest that some drugs with anticoagulant properties may decrease
VD/
VT in patients with ARDS [
35], making this an attractive hypothesis to consider.
At the moment, no data exists on the measurement of physiologic dead space in COVID-19-related ARDS by integrating volumetric capnography plots of eliminated CO2 concentration versus the respective expired tidal volume of a single breath. Since volumetric capnography offers a more in-depth representation of the kinetics of CO2 elimination per breath, the application of longitudinal or time-series models to analyze the effect of CO2 elimination impairment on outcome warrants further research.
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