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Non-invasive oxygenation support in acutely hypoxemic COVID-19 patients admitted to the ICU: a multicenter observational retrospective study

Critical Care volume 26, Article number: 37 (2022) Cite this article

Abstract

Background

Non-invasive oxygenation strategies have a prominent role in the treatment of acute hypoxemic respiratory failure during the coronavirus disease 2019 (COVID-19). While the efficacy of these therapies has been studied in hospitalized patients with COVID-19, the clinical outcomes associated with oxygen masks, high-flow oxygen therapy by nasal cannula and non-invasive mechanical ventilation in critically ill intensive care unit (ICU) patients remain unclear.

Methods

In this retrospective study, we used the best of nine covariate balancing algorithms on all baseline covariates in critically ill COVID-19 patients supported with > 10 L of supplemental oxygen at one of the 26 participating ICUs in Catalonia, Spain, between March 14 and April 15, 2020.

Results

Of the 1093 non-invasively oxygenated patients at ICU admission treated with one of the three stand-alone non-invasive oxygenation strategies, 897 (82%) required endotracheal intubation and 310 (28%) died during the ICU stay. High-flow oxygen therapy by nasal cannula (n = 439) and non-invasive mechanical ventilation (n = 101) were associated with a lower rate of endotracheal intubation (70% and 88%, respectively) than oxygen masks (n = 553 and 91% intubated), p < 0.001. Compared to oxygen masks, high-flow oxygen therapy by nasal cannula was associated with lower ICU mortality (hazard ratio 0.75 [95% CI 0.58–0.98), and the hazard ratio for ICU mortality was 1.21 [95% CI 0.80–1.83] for non-invasive mechanical ventilation.

Conclusion

In critically ill COVID-19 ICU patients and, in the absence of conclusive data, high-flow oxygen therapy by nasal cannula may be the approach of choice as the primary non-invasive oxygenation support strategy.

Background

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has generated an unprecedented demand for intensive care resources to deliver supportive care using non-invasive oxygenation support and invasive mechanical ventilation [1]. Large studies have provided data regarding baseline clinical characteristics of patients admitted to intensive care units (ICU), the need for invasive mechanical ventilation, and outcomes of mechanically ventilated patients [2,3,4,5,6,7,8,9,10].

During the pandemic, patients with acute hypoxemic respiratory failure admitted to the ICU and not in need of emergent intubation of the trachea have mainly been treated with non-invasive oxygenation strategies [1]. These methods include standard non-rebreather oxygen masks (oxygen mask), high-flow oxygen therapy administered through large-bore nasal cannulas (HFT), and non-invasive positive pressure ventilation (NIV) techniques [1, 2, 11]. The choice for using one type of oxygenation support over another has likely been based on local recommendations, personal experience, and availability of devices.

The difference of opinions regarding the optimal technique for non-invasive oxygenation support is ample, but the consequences and relevant clinical outcomes of the various usual care strategies in critically ill COVID-19 patients admitted to an ICU remain unclear [12,13,14,15]. Thus, our main objective was to evaluate the impact of three stand-alone non-invasive oxygenation strategies on intubation rates and ICU mortality at 90 days after admission of critically ill patients with COVID-19-associated acute hypoxemic respiratory failure. We hypothesized that the various non-invasive oxygenation support strategies had no impact on intubation and ICU mortality rates.

Methods

We retrospectively analysed a cohort of patients admitted to ICUs in the Spanish autonomous community of Catalonia between March 14 and April 15, 2020. The official census population of Catalonia at the time was 7,780,479 inhabitants. At the start of the pandemic, the Catalonian public health system had approximately 500 adult ICU beds in its 26 hospitals, and 13 private hospitals had an additional 100–120 ICUs beds for adults. An invitation to participate in the study was sent to the head of department at all the intensive care medicine departments in Catalonia at the end of April 2020. In view of the nature of the study, the ethics committee at the coordinating centre (Hospital Universitari de la Santa Creu i Sant Pau, Barcelona) approved the study protocol (UCIS-CAT 20/151 OBS) and waived the need for informed consent. Participating centres complied with all local requirements.

Inclusion and exclusion criteria

The inclusion criteria were age 18 years or older, patient admitted to an ICU, clinical signs and symptoms compatible with COVID-19 pneumonia, a positive real-time reverse-transcriptase polymerase chain reaction test for SARS-CoV-2 obtained from a nasopharyngeal swab, bilateral infiltrates in the chest X-ray, need for supplemental oxygen to keep arterial oxygen saturation measured with a pulse oximeter (SpO2) above 90%, and admission at one of the participating centres’ ICU between March 14 and April 15, 2020. All patients were candidates for intubation and mechanical ventilation at the time of ICU admission. Decisions regarding intubation of the trachea were based on clinical grounds and judgment of the intensivist in charge.

Only patients who had been treated exclusively with a single non-invasive oxygenation technique during their ICU stay were included in this analysis: oxygen mask at a rate of more than 10 L oxygen/minute, HFT administered through a heated humidifier at a gas flow rate above 30 L/minute and a fraction of inspired oxygen (FiO2) of at least 0.5, or NIV with a FiO2 of at least 0.5 (irrespective of interface, mode and ventilator type used). The choice of non-invasive oxygenation technique was at the discretion of each participating ICU and was based on local recommendations and availability of devices. The day of ICU admission was defined as day 0. Patients were excluded from our analysis in three situations: first, they were intubated and mechanically ventilated before ICU admission because the context and conditions of intubation were unknown; second, they were intubated immediately after ICU admission (within 3 h) because the attempt at non-invasive oxygenation support was considered clinically unsuccessful; and third, they received oxygen supplementation either by nasal prongs or by any combination of oxygen mask, HFT and NIV (Fig. 1). Patients receiving a combination of multiple non-invasive oxygenation support techniques were not analysed as the order of administration and the reasons for change could not be precisely elucidated, thus introducing an unquantifiable allocation bias into the analysis.

Fig. 1
figure 1

Study flowchart

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Data collection

The principal investigator at each centre filled in a standardized case report form for each patient included in the study. Data regarding demographic variables, laboratory data at admission, type of non-invasive and invasive oxygenation support, variables related to gas exchange and respiratory system mechanics obtained shortly after endotracheal intubation, and ICU outcome status at 90 days from ICU admission were processed from the independent case report forms and stored in a single master file. Patients were followed in the ICU until day 90 post-admission or until ICU discharge, whichever occurred first. This file was reviewed independently by three investigators (PDWG, CGI and JM). The principal investigators at each centre were contacted to solve any errors or missing data. Following this process, the master file was closed.

Once intubated, patients who met acute respiratory distress syndrome (ARDS) criteria were classified in mild, moderate and severe oxygenation phenotypes, as described [16]. Driving airway pressure was calculated as the difference between plateau airway pressure and set positive end-expiratory pressure (PEEP). Compliance of the respiratory system was calculated as tidal volume divided by driving airway pressure. The ventilatory ratio and estimated physiological dead-space fraction were calculated as previously described by Sinha et al. [17] and Morales-Quinteros et al. [18], respectively.

Missing data

To account for missing data (Additional file 1: Table s1), we performed multiple imputations by fully conditional specification with predictive mean matching under the missing at random assumption [19]. Further details regarding the imputation method used are presented in Additional file 1: Appendix 1, Additional file 1: Fig. s1, Additional file 1: Fig. S2 and Additional file 1: Table s2.

Statistical analysis

To enable causal inference of the average treatment effect associated with each oxygenation support strategy, we evaluated the baseline covariate balance at ICU admission. Covariate balancing refers to a group of statistical methods used to enable exchangeability of exposed and unexposed subjects to one or multiple treatments in order to minimize confounding. Applying a weight on each individual subject generates a standardized pseudo-population, in which causal treatment effect inference is possible. Nine covariate balancing methods considering all baseline covariates at ICU admission were tested against each other (see Additional file 1: Appendix 2). The best covariate balancing method (standardized mean deviation < 0.1 and variance ratio < 2) was used for all subsequent analyses [20]. A detailed description on the covariate balancing method selection is also shown in Additional file 1: Appendix 2 and Additional file 1: Figs. s3 and s4.

Univariable and multivariable Cox proportional hazard models coupled to the Kaplan–Meier estimator, with and without random frailty terms for between-centre variability effect, were used to analyse the effects of the different oxygenation support strategies on the incidence of intubation and ICU mortality. The effects are represented by hazard ratios and their 95% confidence interval (CI). A time-varying Cox proportional hazards model was used to investigate the effect of time between ICU admission and intubation on 90-day ICU mortality. Patients discharged alive from the ICU were considered to be alive at day 90. Survival distributions among the various oxygenation support strategies were compared using the log-rank test. Proportional hazard assumptions were assessed by inspection of Schoenfeld residuals.

Ventilator-free survival days were defined as the cumulative time in the first 30 days of ICU admission without the need for invasive mechanical ventilation. Patients who died within the first 30 days were assigned 0 ventilator-free days. Population characteristics were compared using analysis of variance or the Kruskal–Wallis test, as appropriate, and the chi-squared test for categorical variables. Statistical analysis was performed through a fully scripted data management pathway using the R environment for statistical computing version 4.1.0. No power calculations were performed due to the observational nature of this cohort study. A two-sided p < 0.05 was considered statistically significant. Values are given as medians and [interquartile ranges] or counts and percentages as appropriate.

Results

Twenty-six centres participated in the study, 20 of the 26 public health system hospitals in Catalonia and 6 of 13 private care centres. The study thus reflects the care provided to patients in 438 of the 620 adult ICU beds available in Catalonia at the beginning of the pandemic, 370 pertaining to the public system and 68 to the private health system (see Additional file 1: Appendix 3).

A total of 1703 critically ill COVID-19 patients were admitted to the participating ICUs between March 14 and April 15, 2020. Figure 1 shows the study flowchart and Additional file 1: Table s3 shows the main characteristics and outcomes for the overall unbalanced population. Three-hundred and nine patients were treated with combinations of non-invasive respiratory support techniques and were excluded from the analysis to allow a clear differentiation between individual therapies. Overall, these patients had a 72% intubation rate (222/309) and the ICU mortality rate up to day 90 post-admission was 27.5% (85/309).

Population analysed and matching analysis

The final analysis included 1093 patients treated with a non-invasive oxygenation support strategy during their ICU stay: 553 (51%) with oxygen mask, 439 (40%) with HFT, and 101 (9%) with NIV. Their main characteristics and outcomes are shown in Additional file 1: Table s4.

Among the various balancing algorithms tested, we employed the targeted stable balancing weights using the optimization algorithm. This algorithm minimized standard mean deviation (< 0.0001) and variance ratios (≤ 1.7) between oxygenation support strategies for all variables and presented consistent and moderate weights (1 ± 0.35) for all patients (Additional file 1: Fig. s5, Additional file 1: Fig. s6, Additional file 1: Fig. s7).

Characteristics of the analysed population

Baseline characteristics across oxygenation support strategies were identical after covariate balancing (Table 1). Patients were mainly male (68%) with a median age of 63 [54–70] years. The median time between hospital and ICU admission was 1 day [0–3 days]. The overall intubation rate was 82% (897/1093). A total of 501 out of 553 (91%) patients treated with oxygen mask were eventually intubated and mechanically ventilated as opposed to 307 out of 439 (70%) of those treated with HFT and 89 out of 101 (88%) of those treated with NIV (p < 0.001). The number of ventilator-free days was higher in the HFT group than in the oxygen mask and NIV groups (p < 0.001).

Table 1 Demographics, baseline characteristics at intensive care unit admission and overall outcome obtained after covariate balancing*
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Table 2 shows the characteristics of patients who eventually required intubation and mechanical ventilation, after covariate balancing. The median time from ICU admission to endotracheal intubation was 0 [0–0] days in the oxygen mask group, 0 [0–1] days in the HFT group, and 0 [0–1] days in the NIV group (p < 0.001). Among the 897 intubated patients, 824 (92%) had an ARDS and this was moderate-to-severe in 571 (69%).

Table 2 Ventilator settings and gas-exchange parameters shortly after intubation and outcome in intubated patients
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Figure 2 shows the cumulative proportion of intubation over time according to the various non-invasive oxygenation support techniques. As compared to oxygen mask, the hazard ratio for intubation in HFT treated patients was 0.45 (95% CI 0.39–0.53) and 0.67 (95% CI 0.53–0.85) for NIV treated patients.

Fig. 2
figure 2

Kaplan–Meier plot of the cumulative proportion of intubation. Kaplan–Meier curve of the cumulative proportion of intubation stratified according to the initial oxygenation support strategy at ICU admission. Oxygen mask, high-flow oxygen therapy and non-invasive ventilation are plotted in red, blue, and green, respectively. p values were calculated by means of the log-rank test. Hazard ratios (HR) were computed using a Cox proportional hazard model and the risk of intubation in the high-flow oxygen therapy and non-invasive ventilation groups was assessed using the oxygen mask group as reference; 95% confidence intervals (CI) are given in parentheses. Table at the bottom presents the patients at risk per time point

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Mortality

The cumulative proportion of ICU survival, evaluated up to 90 days of ICU admission, differed significantly according to the initially chosen non-invasive oxygenation strategy (p = 0.02) (Fig. 3a). As compared to patients treated with an oxygen mask, the hazard ratio for ICU mortality was 0.75 [95% CI 0.58–0.98] for HFT ventilated patients, and 1.21 [95% CI 0.8–1.83] for NIV treated patients.

Fig. 3
figure 3

Kaplan–Meier plot of the cumulative survival in the intensive care unit. Kaplan–Meier curve of the cumulative intensive care survival was stratified according to the initial oxygenation support strategy at admission to the intensive care unit. Subplot (a) refers to all patients included in the analysis. Subplot (b) considers only patients pending probably intubation and invasive mechanical ventilation. Oxygen mask, high-flow oxygen therapy and non-invasive ventilation are plotted in red, blue, and green, respectively. p values were calculated by means of the log-rank test. Hazard ratios (HR) were computed by means of a Cox proportional hazard model. The risk of intensive care unit mortality in the high-flow oxygen therapy and non-invasive ventilation groups was assessed using the oxygen mask group as reference; 95% confidence intervals (CI) are given in parentheses. Table at the bottom shows the patients at risk per time point

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In intubated and mechanically ventilated patients, the 90-day ICU mortality did not differ (p = 0.2) between groups (Fig. 3b).

Secondary analyses

The time from ICU admission to intubation showed no influence on static respiratory system compliance, driving airway pressure, or PaO2/FiO2 ratio as measured shortly after intubation (Additional file 1: Fig. s8, Additional file 1: Fig. s9, Additional file 1: Fig. s10). However, it was associated with an elevated ICU risk of mortality in a time-varying Cox regression model (Additional file 1: Fig. s11).

The Cox proportional hazards model, in covariate balanced population analysis, showed that highly predictive variables for ICU mortality were age, body mass index, high levels of C-reactive protein, procalcitonin, ferritin, D-dimers, and the presence of chronic obstructive pulmonary disease (Additional file 1: Fig. s12). Variables that were highly predictive of ICU mortality in patients, who required invasive mechanical ventilation, are shown in Additional file 1: Fig. s13. In mechanically ventilated patients, high D-dimers were significantly associated with mortality independently of the static respiratory system compliance (Additional file 1: Fig. s14).

Sensitivity analyses assessing 90-day ICU mortality risk in the un-balanced population in crude models (Additional file 1: Fig. s15, Additional file 1: Table s5) and multivariable-adjusted models (Additional file 1: Fig. s16, Additional file 1: Fig. s17, Additional file 1: Fig. s18, Additional file 1: Fig. s19), with and without between-centre random effects term, showed the same associations as the covariate balanced population analysis.

Discussion

In this retrospective, baseline covariate balanced, multicentre cohort of 1093 critically ill non-invasively ventilated COVID-19 ICU patients, 897 (82%) eventually required endotracheal intubation and 310 (28%) died during their ICU stay. The need for endotracheal intubation and invasive mechanical ventilation was significantly lower in patients supported by HFT and NIV than in those receiving oxygen mask therapy at ICU admission. Additionally, patients supported by means of HFT had a lower risk of ICU mortality than patients supported with oxygen masks and NIV.

Debate around the optimal choice of non-invasive oxygenation support in acute hypoxemic respiratory failure has re-emerged within the framework of the COVID-19 pandemic [1, 12, 13, 15, 21,22,23]. The association between the use of HFT or NIV and a reduction in endotracheal intubation rates compared to oxygen mask has yielded ambiguous results [24,25,26]. The two largest randomized trials to date did not find reductions in endotracheal intubation rates benefitted patients supported with HFT or NIV compared to those treated with an oxygen mask [27, 28] These findings may have several explanations: different populations and etiologies of acute hypoxemic respiratory failure, different inclusion and exclusion criteria, non-identical methodological approaches to the use of non-invasive techniques, and different criteria to declare a failed non-invasive oxygenation attempt. Furthermore, the primary outcomes of these studies were not the same.

In SARS-CoV-2-induced hypoxemic respiratory failure, however, the use of HFT and NIV was superior to oxygen mask in limiting progression to invasive mechanical ventilation, supporting our findings [29, 30]. The reason for this efficacy of HFT and NIV is possibly explained by their physiological effects in terms of improvements in gas exchange, decreases in respiratory muscle effort, and a generally ameliorated sensation of dyspnea as compared to oxygen masks [31,32,33,34,35,36,37]. Several mechanisms may explain these beneficial mechanisms. First, the increase in end-expiratory lung volume with some degree of alveolar recruitment induced by PEEP could decrease shunt and consequently improve PaO2 [32,33,34]. Second, as compared to facial oxygen masks, HFT generates a dead space washout of the upper airways, allowing a more homogeneous distribution of tidal volume [32]. Third, HFT induces a decrease in respiratory rate with less effort per breath with unchanged tidal volumes, thus suggesting better lung compliance [32]. These effects may help decrease inspiratory muscle effort, reduce the chemical drive to breath and, in turn, decrease the sensation of dyspnea [35]. It is of note, however, that we are dealing with a single disease (COVID-19) that is typically vasculopathic and accompanied by widespread endothelialitis [38, 39]. Such physiopathological derangements may profoundly modify the ventilation/perfusion relationships and hypoxic pulmonary vasoconstriction [40]. In addition, SARS-CoV-2 exerts direct effects on the carotid bodies and the central nervous system [39], which may alter the central respiratory drive [21].

It is also of interest in our study that we observed lower endotracheal intubation rates in HFT and NIV treated patients than in oxygen mask treated patients. However, only the use of HFT was associated with a reduction in mortality rate. This result is consistent with the landmark trial of Frat et al. [27] but in contrast with the remaining literature [28,29,30]. Recent data [41] indicate that when compared to early intubation, HFT is associated with an increase in ventilator-free days and no differences in hospital mortality. The lower mortality in the HFT group in our study could directly reflect the lower intubation rate in this population.

In our study, we observed a trend towards higher mortality in the NIV group than in the oxygen mask group despite the lower intubation rate. It has been hypothesized that early endotracheal intubation and lung-protective ventilation in patients failing non-invasive oxygenation support and eventually requiring invasive mechanical intubation is associated with a protective effect. In a large series of ADRS patients, Kangelaris et al.[42] showed that mortality in those who were intubated early (on day 1 of ARDS diagnosis) was significantly lower than in those intubated later (between 2 and 4 days after ARDS diagnosis): the adjusted risk of death was 2.37 times higher for late intubation than for early intubation. Other authors, however, did not observe these effects in COVID-19 patients [43, 44]. More than time per se, several authors have suggested that what occurs during non-invasive oxygenation support is relevant. Carteaux [45] and Frat [46] showed that rather than the time to intubation, large tidal volumes—and thus large transpulmonary pressure swings—during NIV are associated with worse outcomes. Hence, one could argue that the total dose of injury (i.e. size of tidal volume and dissipated pressure times duration of ventilation) may eventually be harmful.

Our data show a clear association between the time from ICU admission to intubation and an increased risk of mortality (see Additional file 1: Fig. s11), possibly explaining the trend towards higher mortality in intubated patients previously undergoing NIV support. Additionally, patient self-inflicted lung injury, through the generation of large swings in transpulmonary pressures with excessive tidal volumes and cycling opening and alveolar collapse phenomena, has been postulated as a primary driver of mortality in NIV supported patients [45,46,47]. In addition, other relevant phenomena occurring in non-intubated spontaneously breathing subjects with acute hypoxemic respiratory failure include expiratory airway closure [48], pendelluft with regionally large swings of transpulmonary pressure [49] and, possibly bronchiolotrauma [50]. Finally, the large transpulmonary pressure swings during spontaneous breathing, which generate high pulmonary transmural vascular pressures, also enhance pulmonary oedema [47, 51]. However, in our study, shortly after endotracheal intubation and irrespectively of the non-invasive oxygenation strategy used, we found that respiratory mechanics and gas exchange were comparable between groups. This observation suggests that, when used for a relatively short period of time, none of these three very different non-invasive oxygenation techniques seem to be worse than the other in terms of magnifying the amount of lung injury (as clinically assessed in terms of gas exchange and basic respiratory system mechanics).

Whether the development of a full-blown patient self-inflicted lung injury requires a time-course threshold, as suggested by the increased mortality in patients intubated after more than 3 days under non-invasive respiratory support (Additional file 1: Fig. s11), or it is governed by another form of time-to-event relationship, is unknown [47]. Conversely, patient self-inflicted lung injury could primarily be biotraumatic, exacerbating pulmonary inflammation. And only secondarily, at a later stage (i.e. invasive mechanical ventilation acting as a “second hit”), would this lead to diffuse alveolar damage and alveolar remodelling, and thus impair respiratory system mechanics [30, 47, 49, 52].

Our results stand in contrast with those from a recent randomized controlled trial that compared HFT and NIV as treatment for hypoxemic respiratory failure. The authors found that mortality rates were comparable in the two groups [53]. Nevertheless, it is of note that the patients in Grieco’s study were treated by means of a helmet rather than a facemask. Recent evidence suggests that the limitations of NIV in oxygenation support of hypoxemic respiratory failure are inherent to the facemask interface and that the use of a helmet is associated with fewer air leaks, higher and more stable PEEP levels during prolonged periods of time, improved oxygenation, and reduced inspiratory efforts [31, 54]. Our study has a number of limitations. First, the cohort of patients was collected retrospectively and lacked any degree of randomization, formally impeding an unbiased treatment effect. Nonetheless, the baseline covariate balancing performed enabled mitigation of most objectively assessable confounders. Second, the cohort may be subject to selection bias as during the pandemic outbreak, patients who were treated with the same non-invasive oxygenation techniques but were not admitted into the ICU were not included in the study. Third, in the absence of a study protocol, the choice of oxygenation support strategy and the decision to proceed towards endotracheal intubation and mechanical ventilation were likely variable. It is not known whether the choice of one technique or another was based on clinical decision or equipment availability. Nonetheless, the data presented faithfully represent usual care practice during the pandemic. Fourth, no data on longitudinal inflammatory parameters or pulmonary mechanics data were available, preventing investigation of long-term mechanistic effects associated with the choice of non-invasive oxygenation support strategy. Fifth, neither gas exchange nor other respiratory variables were collected before invasive mechanical ventilation was started. Sixth, mortality was available from the ICU only, impeding analysis of hospital or absolute survival. Nevertheless, the largest proportion of mortality in critically ill patients occurs in the ICU, and our patients were followed for up to 90 days therein. Seventh, no specifications were available regarding the type of NIV interface and settings employed, albeit most probably only facemasks were used. Eighth, the temporal gap between ICU admission and endotracheal intubation was only available on a scale of days, thus impeding a more granular analysis. Ninth, the imbalance in group sizes could have led to a lack of power to recognize higher mortality in the NIV supported group. Tenth, as we do not have data from a more contemporaneous cohort, we cannot assess the dynamic effects of the pandemic—such as changing care practices and new pharmacologic treatments—on the reported effects. Finally, the potential role of unmeasured confounders cannot be excluded.

Conclusion

In critically ill COVID-19 ICU patients with acute hypoxemic respiratory failure, the use of HFT was associated with lower intubation and ICU mortality rates than those in patients supported by means of oxygen mask and NIV. Given the inconclusive data in the ICU population, the choice of HFT as a primary non-invasive oxygenation support strategy seems warranted.

Availablity of data materials

The dataset of the current study is available from the corresponding author on reasonable request.

Abbreviations

COVID-19:

Coronavirus disease-2019

ICU:

Intensive care unit

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

SpO2:

Pulse oximeter oxygen saturation

CI:

Confidence interval

ARDS:

Acute respiratory distress syndrome

PEEP:

Positive end-expiratory pressure

Oxygen mask:

Standard non-rebreather oxygen masks

HFT:

High-flow oxygen therapy administered through large-bore nasal cannulas

NIV:

Non-invasive positive pressure ventilation

CPAP:

Continuous positive airway pressure

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Acknowledgements

The authors thank Carolyn Newey for her assistance in editing the manuscript. In addition to the authors (in bold), the UCIsCAT study group includes the following collaborators: Hospital Universitari General de la Vall d’Hebron, Barcelona: R.Ferrer, O.Roca, X. Nuvials, J.C Ruiz, E. Papiol. Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat : R. Máñez, V.D Gumicio. Hospital Clínic i Provincial de Barcelona, Barcelona: E.Sandoval, G.Muñoz, D.Toapanta, P.Castro, J. Osorio. Hospital del Mar, Barcelona: J.R Masclans, R. Muñoz-Bermúdez, F. Parrilla, P. Pérez-Teran, J. Marin-Corral. Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despí: A. Mas, B. Cancio, S.Hernández-Marín, M.R. Koborzan , C.A Briones. Hospital Mútua de Terrassa, Terrassa: J. Trenado. Althaia (Xarxa Assistencial Universitària de ManresaM. Martínez), Manresa: R.Fernández. Hospital Universitari Doctor Josep Trueta de Girona, Girona: J.M Sirvent, P. Sebastian, X. Saiz. Hospital General De Cataluña, Sant Cugat del Vallès: M. Martínez. Hospital Universitari Sagrat Cor—Grup Quirónsalut, Barcelona: M. Ibarz. Hospital General de Granollers, Granollers: P.Garro, C.Pedrós, E.Vendrell. Hospital General de Vic (Consorci Hospitalari de Vic), Vic: J.L Lopera. Hospital Universitari de Tarragona Joan XXIII, Tarragona: M. Bodí, A. Rodríguez, G. Moreno. Hospital de Mataró, Mataró: J.C Yébenes-Reyes. Hospital d’Igualada, Igualada: C. Triginer. Hospital Sant Joan de Reus, Reus: I. Vallverdú. Hospital de Santa Caterina, Salt: A. Baró, M. Morales. Hospital de Sant Pau i Santa Tecla, Tarragona: F. Bodí. Hospital HM Delfos, Barcelona: P.Saludes, J-R.Cervelló. Hospital El Pilar—Grup Quirónsalut, Barcelona: M. Valencia. Hospital de Tortosa Verge de la Cinta, Tortosa: F. Roche-Campo, D. Franch-Llasat. Clínica Sagrada Família, Barcelona: A.Huerta, P.Santigosa. Hospital Sant Joan de Déu, Esplugues de Llobregat: F.J Cambra, S. Benito. Hospital Santa María, Lleida: C.Barberà. Hospital ASEPEYO de Barcelona, Sant Cugat del Vallés: J.Echevarría. Hospital de la Santa Creu i Sant Pau, Barcelona: J. Mancebo, P.Vera, J-A.Santos, J.Baldirà, A-J.Betbesé, M.Izura, I.Morán, J-C.Suárez, L.Zapata, N.Rodríguez, M.Torrens, A.Cordón, C.Gomila, M.Flores, A.Segarra, M.Morales, L.Mateo, M.Martos, C.González-Isern (Coordinating centre)

Funding

This study had no funding.

Author information

Author notes

  1. Pedro David Wendel-Garcia and Jordi Mancebo have contributed equally.

Authors and Affiliations

  1. Institute of Intensive Care Medicine, University Hospital of Zurich, Zurich, Switzerland

    Pedro David Wendel-Garcia

  2. Intensive Care Department, Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despí, Spain

    Arantxa Mas

  3. Medical Technology Department, Hospital de La Santa Creu I Sant Pau, Barcelona, Spain

    Cristina González-Isern

  4. Intensive Care Department/SODIR Research Group, Hospital Universitari General de La Vall d’Hebron, Barcelona, Spain

    Ricard Ferrer

  5. Intensive Care Department, L’Hospitalet de Llobregat, Barcelona, Spain

    Rafael Máñez

  6. Intensive Care Department, Hospital del Mar, GREPAC Research Group - IMIM, Department Ciències, Experimentals I de La Salut (DCEXS) UPF, Barcelona, Spain

    Joan-Ramon Masclans

  7. Cardiovascular Surgery Department, Hospital Clínic de Barcelona, Barcelona, Spain

    Elena Sandoval

  8. Intensive Care Department, Hospital de La Santa Creu I Sant Pau, Barcelona, Spain

    Paula Vera & Jordi Mancebo

  9. Intensive Care Department, Hospital Mútua de Terrassa, Terrassa, Spain

    Josep Trenado

  10. Intensive Care Department, Althaia, Xarxa Assistencial Universitària de Manresa, Manresa, Spain

    Rafael Fernández

  11. Intensive Care Department, Hospital Universitari Doctor Josep Trueta de Girona, Girona, Spain

    Josep-Maria Sirvent

  12. Intensive Care Department, Hospital General De Cataluña, Sant Cugat del Vallès, Spain

    Melcior Martínez

  13. Intensive Care Department, Hospital Universitari Sagrat Cor - Grup Quirónsalut, Barcelona, Spain

    Mercedes Ibarz

  14. Intensive Care Department, Hospital General de Granollers, Granollers, Spain

    Pau Garro

  15. Intensive Care Department, Hospital General de Vic, Consorci Hospitalari de Vic, Vic, Spain

    José Luis Lopera

  16. Intensive Care Department, Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain

    María Bodí

  17. Intensive Care Department, Hospital de Mataró, Mataró, Spain

    Joan Carles Yébenes-Reyes

  18. Intensive Care Department, Hospital d’Igualada, Igualada, Spain

    Carles Triginer

  19. Intensive Care Department, Hospital Sant Joan de Reus, Reus, Spain

    Imma Vallverdú

  20. Intensive Care Department, Hospital de Santa Caterina, Salt, Spain

    Anna Baró

  21. Intensive Care Department, Hospital de Sant Pau I Santa Tecla, Tarragona, Spain

    Fernanda Bodí

  22. Intensive Care Department, Hospital HM Delfos, Barcelona, Spain

    Paula Saludes

  23. Intensive Care Department, Hospital El Pilar - Grup Quirónsalut, Barcelona, Spain

    Mauricio Valencia

  24. Intensive Care Department, Hospital de Tortosa Verge de La Cinta, Tortosa, Spain

    Ferran Roche-Campo

  25. Intensive Care Department, Clínica Sagrada Família, Barcelona, Spain

    Arturo Huerta

  26. Pediatric Intensive Care Department, Hospital Sant Joan de Déu de Barcelona, Esplugues de Llobregat, Spain

    Francisco José Cambra

  27. Intensive Care Department, Hospital Santa Maria, Lleida, Spain

    Carme Barberà

  28. Intensive Care Department, Hospital ASEPEYO de Barcelona, Sant Cugat del Vallés, Spain

    Jorge Echevarria

  29. Intensive Care Department Hospital, Universitario de Getafe, CIBER Enfermedades Respiratorias, CIBERES (Spain), Madrid, Spain

    Óscar Peñuelas

  30. Institut d, Investigació Biomèdica Sant Pau, ’, Servei Medicina Intensiva, Hospital Universitari Sant Pau, Barcelona, Spain

    Jordi Mancebo

  31. Hospital Universitari General de La Vall d’Hebron, Barcelona, Spain

    R. Ferrer, O. Roca, X. Nuvials, J. C. Ruiz & E. Papiol

  32. Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain

    R. Máñez & V. D. Gumicio

  33. Hospital Clínic I Provincial de Barcelona, Barcelona, Spain

    E. Sandoval, G. Muñoz, D. Toapanta, P. Castro & J. Osorio

  34. Hospital del Mar, Barcelona, Spain

    J. R. Masclans, R. Muñoz-Bermúdez, F. Parrilla, P. Pérez-Teran & J. Marin-Corral

  35. Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despí, Spain

    A. Mas, B. Cancio, S. Hernández-Marín, M. R. Koborzan & C. A. Briones

  36. Hospital Mútua de Terrassa, Terrassa, Spain

    J. Trenado

  37. Althaia (Xarxa Assistencial Universitària de Manresa), Manresa, Spain

    R. Fernández

  38. Hospital Universitari Doctor Josep Trueta de Girona, Girona, Spain

    J. M. Sirvent, P. Sebastian & X. Saiz

  39. Hospital General De Cataluña, Sant Cugat del Vallès, Spain

    M. Martínez

  40. Hospital Universitari Sagrat Cor - Grup Quirónsalut, Barcelona, Spain

    M. Ibarz

  41. Hospital General de Granollers, Granollers, Spain

    P. Garro, C. Pedrós & E. Vendrell

  42. Hospital General de Vic (Consorci Hospitalari de Vic), Vic, Spain

    J. L. Lopera

  43. Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain

    M. Bodí, A. Rodríguez & G. Moreno

  44. Hospital de Mataró, Mataró, Spain

    J. C. Yébenes-Reyes

  45. Hospital d’Igualada, Igualada, Spain

    C. Triginer

  46. Hospital Sant Joan de Reus, Reus, Spain

    I. Vallverdú

  47. Hospital de Santa Caterina, Salt, Spain

    A. Baró & M. Morales

  48. Hospital de Sant Pau I Santa Tecla, Tarragona, Spain

    F. Bodí

  49. Hospital HM Delfos, Barcelona, Spain

    P. Saludes & J.-R. Cervelló

  50. Hospital El Pilar - Grup Quirónsalut, Barcelona, Spain

    M. Valencia

  51. Hospital de Tortosa Verge de La Cinta, Tortosa, Spain

    F. Roche-Campo & D. Franch-Llasat

  52. Clínica Sagrada Família, Barcelona, Spain

    A. Huerta & P. Santigosa

  53. Hospital Sant Joan de Déu, Esplugues de Llobregat, Spain

    F. J. Cambra & S. Benito

  54. Hospital Santa María, Lleida, Spain

    C. Barberà

  55. Hospital ASEPEYO de Barcelona, Sant Cugat del Vallés, Spain

    J. Echevarría

  56. Hospital de La Santa Creu I Sant Pau, Barcelona, Spain

    J. Mancebo, P. Vera, J.-A. Santos, J. Baldirà, A.-J. Betbesé, M. Izura, I. Morán, J.-C. Suárez, L. Zapata, N. Rodríguez, M. Torrens, A. Cordón, C. Gomila, M. Flores, A. Segarra, M. Morales, L. Mateo, M. Martos & C. González-Isern

Authors
  1. Pedro David Wendel-Garcia
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  2. Arantxa Mas
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  3. Cristina González-Isern
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  4. Ricard Ferrer
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  5. Rafael Máñez
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  6. Joan-Ramon Masclans
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  7. Elena Sandoval
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  8. Paula Vera
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  9. Josep Trenado
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  10. Rafael Fernández
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  11. Josep-Maria Sirvent
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  12. Melcior Martínez
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  13. Mercedes Ibarz
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  14. Pau Garro
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  15. José Luis Lopera
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  16. María Bodí
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  17. Joan Carles Yébenes-Reyes
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  18. Carles Triginer
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  19. Imma Vallverdú
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  20. Anna Baró
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  21. Fernanda Bodí
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  22. Paula Saludes
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  23. Mauricio Valencia
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  24. Ferran Roche-Campo
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  25. Arturo Huerta
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  26. Francisco José Cambra
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  27. Carme Barberà
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  28. Jorge Echevarria
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  29. Óscar Peñuelas
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  30. Jordi Mancebo
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Consortia

for the UCIsCAT study group

  • R. Ferrer
  • , O. Roca
  • , X. Nuvials
  • , J. C. Ruiz
  • , E. Papiol
  • , R. Máñez
  • , V. D. Gumicio
  • , E. Sandoval
  • , G. Muñoz
  • , D. Toapanta
  • , P. Castro
  • , J. Osorio
  • , J. R. Masclans
  • , R. Muñoz-Bermúdez
  • , F. Parrilla
  • , P. Pérez-Teran
  • , J. Marin-Corral
  • , A. Mas
  • , B. Cancio
  • , S. Hernández-Marín
  • , M. R. Koborzan
  • , C. A. Briones
  • , J. Trenado
  • , R. Fernández
  • , J. M. Sirvent
  • , P. Sebastian
  • , X. Saiz
  • , M. Martínez
  • , M. Ibarz
  • , P. Garro
  • , C. Pedrós
  • , E. Vendrell
  • , J. L. Lopera
  • , M. Bodí
  • , A. Rodríguez
  • , G. Moreno
  • , J. C. Yébenes-Reyes
  • , C. Triginer
  • , I. Vallverdú
  • , A. Baró
  • , M. Morales
  • , F. Bodí
  • , P. Saludes
  • , J.-R. Cervelló
  • , M. Valencia
  • , F. Roche-Campo
  • , D. Franch-Llasat
  • , A. Huerta
  • , P. Santigosa
  • , F. J. Cambra
  • , S. Benito
  • , C. Barberà
  • , J. Echevarría
  • , J. Mancebo
  • , P. Vera
  • , J.-A. Santos
  • , J. Baldirà
  • , A.-J. Betbesé
  • , M. Izura
  • , I. Morán
  • , J.-C. Suárez
  • , L. Zapata
  • , N. Rodríguez
  • , M. Torrens
  • , A. Cordón
  • , C. Gomila
  • , M. Flores
  • , A. Segarra
  • , M. Morales
  • , L. Mateo
  • , M. Martos
  •  & C. González-Isern

Contributions

PDWG, CGI and JM take responsibility for the integrity of the data and the accuracy of the data analysis. PDWG, CGI and JM conceived, designed and coordinated the research. PDWG, CGI and JM were responsible for data acquisition and interpretation. PDWG performed statistical analysis. PDWG, CGI and JM drafted the manuscript. All authors participated in collection of data, critically revised the draft of the manuscript, reviewed and approved the final version and agreed to submit the manuscript for publication.

Corresponding author

Correspondence to Jordi Mancebo.

Ethics declarations

Ethics approval and consent to participate

The ethics committee at the coordinating centre (Hospital Universitari de la Santa Creu i Sant Pau, Barcelona) approved the study protocol (UCIS-CAT 20/151 OBS) and waived the need for informed consent. Participating centres complied with all local requirements.

Consent for publication

Not applicable.

Competing interests

P.D Wendel-Garcia has nothing to disclose. A. Mas declares teaching fees from Fundació Parc Tauli, outside the submitted work. C. González-Isern has nothing to disclose. R. Ferrer declares personal fees from MSD, GSK, Alexion, Pfizer, Shionogi, Menarini and Gilead, outside the submitted work. R. Máñez has nothing to disclose. J.R. Masclans declares grants and research support from Fisher&Paykel and fees from Dextro payed to his Institution, outside the submitted work. E. Sandoval, P. Vera, J. Trenado, R. Fernández, J.M. Sirvent, M. Martínez, M. Ibarz, P. Garro, J.L. Lopera, M. Bodí, J.C. Yébenes-Reyes, C. Triginer, I. Vallverdú, A. Baró, F. Bodí, P. Saludes, M. Valencia, F. Roche-Campo, A. Huerta, F.J. Cambra, C. Barberá, J. Echeverria, and O. Peñuelas have nothing to disclose. J. Mancebo declares personal fees from Medtronic and Janssen, a research grant from Covidien/Medtronic and the Canadian Institutes of Health Research and he is consultant for Vyaire, all outside the submitted work.

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Supplementary Information

Additional file 1: e-Table 1:

Percentage of missing values. e-Appendix 1: Multiple imputations of missing data and technique break down. e-Figure 1: Multiple imputation - convergence plots. e-Figure 2: Multiple imputation - distribution plots. e-Table 2: Imputation model fit. e-Appendix 2: Extended Statistical Methodology. e-Figure 3: Covariate balancing models - model fit comparison. e-Figure 4: Covariate balancing models - weights comparison. e-Appendix 3: Study information. e-Table 3: Overall un-balanced population. e-Table 4: Un-balanced study population. e-Figure 5: Final covariance balancing model - model fit. e-Figure 6: Final covariate balancing model - individual variable distributions. e-Figure 7: Final covariate balancing model - model weights. e-Figure 8: Static compliance versus time from ICU admission until intubation. e-Figure 9: Driving pressure versus time from ICU admission until intubation. e-Figure 10: PaO2/ FiO2 ratio versus time from ICU admission until intubation. e-Figure 11: Cox time varying model for influence of time until intubation on ICU mortality. e-Figure 12: Multivariable Cox proportional hazards model for in-ICU mortality (overall and covariance balanced). e-Figure 13: Multivariable Cox proportional hazards model for in-ICU mortality (only intubated and covariance balanced). e-Figure 14: Kaplan-Meier curve for ICU mortality – compliance vs. D-dimers. e-Figure 15: Kaplan-Meier curve for ICU mortality in unbalanced population. e-Table 5: Cox mixed-effects model with between-center random effects term (unbalanced). e-Figure 16: Multivariable Cox proportional hazards model for in-ICU mortality (overall and unbalanced). e-Figure 17: Multivariable Cox proportional hazards model for in-ICU mortality (only intubated and unbalanced). e-Figure 18: Multivariable Cox mixed-effects model with between-center random effects term for in-ICU mortality (overall study population – unbalanced). e-Figure 19: Multivariable Cox mixed-effects model with between-center random effects term for in-ICU mortality (only intubated – unbalanced). e-Appendix 4: Formulas.

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Wendel-Garcia, P.D., Mas, A., González-Isern, C. et al. Non-invasive oxygenation support in acutely hypoxemic COVID-19 patients admitted to the ICU: a multicenter observational retrospective study. Crit Care 26, 37 (2022). https://doi.org/10.1186/s13054-022-03905-5

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Keywords

  • COVID-19
  • Intensive care
  • Non-invasive oxygenation
  • Acute hypoxemic respiratory failure

Critical Care

ISSN: 1364-8535