Around 5% of coronavirus disease-2019 (COVID-19) patients develop a critical form of the disease leading to ICU admission (1 , 2
Initial guidelines advised against the use of noninvasive positive pressure ventilation (NIPPV) or high-flow nasal cannula (HFNC) as they are aerosol-generating procedures and, thus, a risk of infection among healthcare workers (3 4 5
There is no clear-cut evidence of the greater effect of noninvasive oxygen support (NI-OS), including NIPPV, HFNC, continuous positive airway pressure (CPAP), and oxygen delivered through a nonrebreather face mask, than that of early IMV for COVID-AHRF management. NI-OS can improve patient outcome by avoiding intubation (6–8 9 10 11–13 12 13 14
In the absence of randomized clinical trials to assess the causal effect of IMV on mortality during the COVID pandemic, analysis of observational longitudinal studies is a suitable alternative (15 , 16
The aim of this study was to assess the effect of early IMV in COVID-AHRF on 60-day mortality, using a large high-quality database and applying an inverse probability of treatment weight (IPTW) weighted Cox-survival model.
MATERIALS AND METHODS
Data Source
The study used data from the French prospective multicenter OutcomeRea database (n = 11 ICUs). The methods of data collection and quality of the database have been described in detail elsewhere (17
Study Population
Patients over 18 years were eligible for inclusion in the analysis if they were admitted to one of the participant ICUs belonging to the OutcomeRea network and had at admission an AHRF related to severe COVID-19 pneumonia defined as the combination of: 1) radiological features compatible with this diagnosis, 2) Pao 2 /Fio 2 ratio ≤ 300 mm Hg, and 3) a positive severe acute respiratory syndrome coronavirus-2 test using reverse-transcriptase polymerase chain reaction.
Patients were excluded if they were referred from another ICU or intermediate are unit, when a decision was made to discontinue life-sustaining treatments during the first 2 calendar days after ICU admission, if ICU length of stay was less than or equal to 2 days and if they had a shock or a Glasgow Coma Scale (GCS) less than or equal to 12 on ICU admission.
Data Collection
All data were prospectively collected and comprised those recorded on ICU admission, several variables collected throughout the ICU stay and outcomes (demographics, chronic disease/comorbidities as assessed by the Knaus Scale (18 19 20
Definitions, Group Assignment, and Respiratory Support Modalities
COVID-AHRF was classified into three categories based on the severity of hypoxemia at admission: mild (200 < Pao 2 /Fio 2 ≤ 300 mm Hg), moderate (100 < Pao 2 /Fio 2 < 200 mm Hg), and severe (Pao 2 /Fio 2 ≤ 100 mm Hg) (21
The early IMV group comprised all patients who received IMV within the first 2 calendar days in after ICU admission and the nonearly IMV group comprised all other patients who received at least one of these NI-OSs: NIPPV, HFNC, CPAP, and oxygen delivered through a nonrebreather face mask and not earlier than the third day after ICU admission.
Strategies for IMV and descriptions of the NI-OS techniques are given in the Online Data Supplement (https://links.lww.com/CCX/A495
Statistical Analysis
Patient characteristics were expressed as n (%) for categorical variables and median (interquartile range [IQR]) for continuous variables. Comparisons were made with exact Fisher tests for categorical variables and Wilcoxon tests for continuous variables.
To estimate the average causal effect of early IMV on day-60 mortality, on longitudinal observational data, we used an IPTW estimator, which is the inverse of the patients’ predicted probability of being in the early IMV group on the basis of their baseline covariates.
IPTW estimator is a statistical tool that allows causal inference on observational data. It was developed nearly 20 years ago (22–26 https://links.lww.com/CCX/A495
The impact of early IMV on day-60 mortality was estimated by a two-step process: 1) weight estimation by the IPTW estimator and 2) estimation of the impact of early IMV on day-60 mortality by a weighted Cox model. In the first step, the weight model, a nonparsimonious multivariate logistic regression model, was constructed to estimate each patient’s predicted probability of being in the early IMV group. We included in the weight model the following baseline covariates, recorded at admission and not affected by study groups: period of admission, time between symptom onset and ICU admission, time between hospital and ICU admission, age, gender, body mass index, comorbidities including presence of chronic liver failure, cardiovascular, respiratory and kidney chronic diseases, immunosuppression, clinical and laboratory features at admission, T greater than 39°C, renal SOFA item (> 2), GCS < 15, Pao 2 /Fio 2 ratio, respiratory rate, lactatemia, lymphocyte count, ferritin, d -dimer plasma level, C-reactive protein serum level, and treatments received at admission including lopinavir ritonavir, hydroxychloroquine, tocilizumab, anakinra, and corticosteroids received at admission. In the model, continuous variables were kept linearly unless in the absence of log linearity. All variables included in the weight model reflected knowledge available at baseline. To avoid extreme weights, we used stabilized weights, and to ensure the positivity, assumption was respected, weights were truncated at the 1–99th percentile (27 https://links.lww.com/CCX/A495 o 2 /Fio 2 ratio at admission. Characteristics of the population by centers are reported in Table E1 (https://links.lww.com/CCX/A495 o 2 /Fio 2 less than 150 mm Hg at admission, and 3) with a Pao 2 /Fio 2 greater than 150 mm Hg at admission. These groups were defined prior to the statistical analyses. For all tests, a two-sided α of 0.05 was considered to be significant. Missing baseline variables were handled by median. All statistical analyses were performed with the SAS software, Version 9.4 (SAS Institute, Cary, NC).
RESULTS
Patients and Baseline Characteristics
From February 15, 2020, to May 1, 2020, 400 patients with laboratory confirmed COVID-19 were admitted to ICUs of the OutcomeRea network. Of the 245 included in the study (Fig. 1 Table E2 , https://links.lww.com/CCX/A495 o 2 /Fio 2 ratio was 121 mm Hg (90–174) and 206 patients (84%) had moderate-to-severe AHRF. Overall, 117 patients (47.8%) received IMV (early IMV group), oxygen by mask or nasal prongs 16 (6.6%), HFNC 85 (34.6%), CPAP 18 (7.4%), and NIPPV 9 (3.6%). After intubation, their median (IQR) tidal volume was 6 mL/kg (5.9–6.4 mL/kg) with a median (IQR) compliance of 36.7 mL/mm Hg (27.6–53.3 mL/mm Hg). Ninety-five patients (38.8%) received lopinavir-ritonavir, 21 patients (8.6%) hydroxychloroquine, 22 patients (9%) tocilizumab, 22 patients (9%) anakinra, and 68 patients (28%) corticosteroids. The comparison of baseline characteristics between the early and the nonearly IMV groups is shown in Table E3 (https://links.lww.com/CCX/A495 p < 0.01, p < 0.01, and p < 0.01, respectively).
Figure 1.: Flowchart. AHRF = acute hypoxemic respiratory failure, COVID-19 = coronavirus disease 2019, GCS = Glasgow Coma Scale, IMV = invasive mechanical ventilation, LOS = length of stay.
Propensity Score Model Development
Propensity scores ranged from 0.01 to 0.92 and from 0.02 to 0.97 in the no-early IMV and in the early IMV groups, respectively, with 93.8% in the region of common support (0.02–0.92) (Fig. E1 , https://links.lww.com/CCX/A495 Table E4 (https://links.lww.com/CCX/A495 Fig. 2
Figure 2.: Standardized differences of variables used to generate the propensity score before and after proportional-hazard model weighted by inverse probability (IPTW). Propensity scores ranged from 0.01 to 0.92 and from 0.02 to 0.97 in the no early invasive mechanical ventilation (IMV) and in the early IMV groups, respectively, with 93.8% in the region of common support (0.02–0.92). All the covariates in the planned propensity score were kept in the final model. After applying IPTW, all covariates in the planned propensity score had weighted standardized differences below 10%, which is in favor of an equilibration of the covariates between the subgroups and ensures the exchangeability at baseline for these confounders. ACE = angiotensin-converting-enzyme inhibitors, SOFA = Sequential Organ Failure Assessment.
Follow-Up and Outcomes
The day-60-mortality in the whole study population was 31.8%. It was higher in the early than in the nonearly IMV group: 42.7% versus 21.9% (pval < 0.01), respectively.
After weighted Cox model analysis, the risk of death at day 60 was higher in patients receiving early IMV (HRw = 1.74, 95% CI, 1.07–2.83; p = 0.03). Similar results were observed in a sensitivity analysis using truncated HRw (Table E5 , https://links.lww.com/CCX/A495 Fig. 3
Figure 3.: Effect of early invasive mechanical ventilation on ICU death of patients in the main cohort and in different subgroups (sensitivity analyses).
In other sensitivity analyses, early IMV was also associated with day-60-mortality (Tables E6 and E7 , and Fig. E2 , https://links.lww.com/CCX/A495
In addition, we found that in the case of delayed intubation, that is, intubation after the first 2 calendar days after ICU admission, the patients in the late-IMV group (n = 45, 18.4%) had a similar outcome to those in the early IMV group, with a day-60-mortality of 42.2% and 42.7%, respectively. Patients without any IMV (n = 83, 33.9%) had the best survival rate during the ICU stay, with a day-60 mortality of 10.8% (Table E8 , https://links.lww.com/CCX/A495
DISCUSSION
Our study adds to current knowledge on the management of COVID-19 patients. It shows that using IMV during the first 2 calendar days after ICU admission in critically ill COVID-19 patients with AHRF was associated with increased day-60 mortality as compared to initial use of NI-OS. It also shows that patients intubated later because of failure of the noninvasive oxygenation strategy had a similar outcome to those ventilated early. The day-60 survival of patients with this successful conservative noninvasive strategy was better.
Deciding when to initiate IMV in critically ill patients with AHRF is challenging. The benefits of IMV must be weighed against its inherent risks (9 28 29 11 , 12 , 30 12 31 , 32 33–35
The need for IMV varied widely, from 29.1% to 89.9%, among patients admitted to the ICU with COVID-AHRF. IMV is invariably associated with high mortality ranging from 16% to 78% (2 , 4 , 31 , 36 , 37 36 p < 0.01) than those treated with IMV. Two recent retrospective cohort studies were not able to show a deleterious effect of intubation and subsequent IMV. Demoule et al (38 n = 146) versus 72% (95% CI, 64–79%) in patients without HFNC (n = 233)—but did not affect case fatality. Hernandez-Romieu et al (39
Hospital capacity in France was exceeded during the pandemic. Some patients were intubated early to be transferred safely to the ICU and consequently did not undergo HFNC or other techniques to delay intubation. Furthermore, the recommendations, published at the beginning of the pandemic (3
In our study, higher mortality was observed in the early IMV group. Several factors could have contributed to this result. First, most patients in the nonearly IMV group received HFNC for oxygenation and respiratory support (62.4%) and very few received NIPPV. In de novo AHRF, the impact of HFNC on survival is uncertain (32 , 40 41 , 42 29 , 40 io 2 , delivers a positive end-expiratory pressure value maintenance around 4 mm Hg, and can adjust independently the amount of Fio 2 and the flow rate in an attempt to match the patient’s oxygenation requirements and inspiratory flow demands. Thus, HFNC seems to improve oxygenation while reducing excessive respiratory efforts, pleural pressure swings, and exacerbation of lung injury (P-SILI) (41
Second, NI-OS significantly reduced nosocomial infections. Several studies have already shown that NIPPV in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease or severe cardiogenic pulmonary edema was associated with improved survival and reduction of nosocomial infections (43 , 44
One important finding of our study was that patients who ultimately did need IMV according to physicians had a high mortality rate. This underlines again the complexity of the timing of intubation in patients suffering from COVID-AHRF. This finding is consistent with the results from the Lombardy cohort (36 p < 0.01). The mortality of the patients undergoing subsequent intubation was similar to that of patients who were treated with mechanical ventilation at ICU admission: HR for IMV versus NIV failure, 1.20; 95% CI, 0.95–1.53; p = 0.12). Tools have been developed to avoid a deleterious delayed intubation in patients suffering from COVID-AHRF (45 , 46 io 2 to respiratory rate is an index that can help to identify those with low and those with high risk for intubation (47
Our study has certain limitations. First, although the question of whether noninvasive oxygenation techniques can be administered outside ICUs is crucial in the case of limited ICU capacity and lack of ventilators, we focused on the impact of early IMV in ICU patients. Consequently, the study does not address the potential role of NI-OS in intermediate care units or wards. However, if hospitals and physicians cannot do otherwise, NI-OS could be an alternative in such a pandemic context, provided that strict protocols are discussed beforehand among the various hospital stakeholders involved. Second, most of our patients were managed with HFNC, and none were under Helmet, which limits the external validity of our results. Third, we pooled all the noninvasive ventilation procedures and could not compare HNFC with either NIPPV or CPAP. Fourth, several data could not be collected or determined retrospectively, including the phenotype (L or H) of the COVID-AHRF, the tidal volume in patients under noninvasive oxygenation techniques, and the medical reasons leading physicians to intubate or not the patients and the timing of the procedure. Finally, our study was only observational. As a result, although we tried to consider most confounding factors, our model could still be biased mostly because of unmeasured confounding. Then, to avoid selection bias and bias due to immortal time, we excluded all the patients transferred from another ICU or intermediate care unit who might have benefitted from NI-OS before ICU admission and also all patients with shock or neurologic failure at admission, for whom intubation is recommended. In addition, all our sensitivity analyses yielded the same results.
CONCLUSIONS
In ICU patients admitted with COVID-19-induced acute hypoxemic respiratory failure, early IMV was associated with an increased day-60 mortality. Those results deserved to be confirmed by randomized controlled trials.
ACKNOWLEDGMENT
The OutcomeRea study group members thank Celine Feger, MD, from Emibiotech for assistance in medical writing.
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APPENDIX. OUTCOMEREA STUDY GROUP
Jean-François Timsit, Elie Azoulay, Maïté Garrouste-Orgeas, Jean-Ralph Zahar, Bruno Mourvillier, Christophe Clec’h, Corinne Alberti, Stephane Ruckly, Sébastien Bailly, Aurélien Vannieuwenhuyze, Christophe Adrie, Carole Agasse, Bernard Allaouchiche, Olivier Andremont, Pascal Andreu, Laurent Argaud, Claire Ara-Somohano, Elie Azoulay, Francois Barbier, Déborah Boyer, Jean-Pierre Bedos, Thomas Baudry, Jérome Bedel, Julien Bohé, Lila Bouadma, Jeremy Bourenne, Noel Brule, Cédric Brétonnière, Frank Chemouni, Christine Cheval, Julien Carvelli, Elisabeth Coupez, Martin Cour, Claire Dupuis, Etienne de Montmollin, Loa Dopeux, Anne-Sylvie Dumenil, Jean-Marc Forel, Marc Gainnier, Charlotte Garret, Dany Goldgran-Tonedano, Steven Grangé, Antoine Gros, Hédia Hammed, Akim Haouache, Romain Hernu, Tarik Hissem, Vivien Hong Tuan Ha, Sébastien Jochmans, Jean-Baptiste Joffredo, Hatem Kallel, Guillaume Lacave, Virgine Laurent, Alexandre Lautrette, Clément Le bihan Eric Magalhaes, Virgine Lemiale, Guillaume Marcotte, Jordane Lebut, Maxime Lugosi, Sibylle Merceron, Benoît Misset, Mathild Neuville, Laurent Nicolet, Johanna Oziel, Laurent Papazian, Juliette Patrier, Benjamin Planquette, Aguila Radjou, Marie Simon, Romain Sonneville, Jean Reignier, Bertrand Souweine, Carole Schwebel, Shidasp Siami, Romain Sonneville, Nicolas Terzi, Gilles Troché, Marie Thuong, Guillaume Thierry, Marion Venot, Sondes Yaacoubi, Olivier Zambon, Julien Fournier, Stéphanie Bagur, Mireille Adda, Vanessa Vindrieux, Sylvie de la Salle, Pauline Enguerrand, Vincent Gobert , Stéphane Guessens, Helene Merle, Nadira Kaddour, Boris Berthe, Samir Bekkhouche, Kaouttar Mellouk, Mélaine Lebrazic, Carole Ouisse, Diane Maugars, Christelle Aparicio, Igor Theodose, Manal Nouacer, Veronique Deiler, Fariza Lamara, Myriam Moussa, Atika Mouaci, and Nassima Viguier.
