Next Article in Journal
A Multicenter Network Analysis Examining the Psychological Effects of the COVID-19 Pandemic on Patients with Eating Disorders versus Their Healthy Siblings
Previous Article in Journal
Efficacy of Fu’s Subcutaneous Needling in Treating Soft Tissue Pain of Knee Osteoarthritis: A Randomized Clinical Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 11
Issue 23
10.3390/jcm11237186
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Acute Improvements of Oxygenation with Cpap and Clinical Outcomes in Severe COVID-19 Pneumonia: A Multicenter, Retrospective Study

by
Stefano Pini
1,
Dejan Radovanovic
1,
Marina Saad
1,
Marina Gatti
1,2,
Fiammetta Danzo
1,2,
Michele Mondoni
3,4,
Stefano Aliberti
5,6,
Stefano Centanni
3,4,
Francesco Blasi
7,8,
Davide Alberto Chiumello
9,10 and
Pierachille Santus
1,2,*
1
Division of Respiratory Diseases, ASST Fatebenefratelli-Sacco, Ospedale Luigi Sacco, Polo Universitario, 20157 Milano, Italy
2
Department of Biomedical and Clinical Sciences (DIBIC), Università Degli Studi Di Milano, 20157 Milano, Italy
3
Respiratory Unit, ASST Santi Paolo e Carlo, San Paolo Hospital, 20142 Milano, Italy
4
Dipartimento di Scienze Della Salute, Università Degli Studi Di Milano, 20146 Milano, Italy
5
Department of Biomedical Sciences, Humanitas University, 20090 Pieve Emanuele, Italy
6
Respiratory Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
7
Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milano, Italy
8
Department of Pathophysiology and Transplantation, Università Degli Studi Di Milano, 20122 Milano, Italy
9
Department of Anesthesia and Intensive Care, ASST Santi Paolo e Carlo, San Paolo University Hospital, 20142 Milano, Italy
10
Coordinated Research Center on Respiratory Failure, Università Degli Studi Di Milano, 20142 Milano, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(23), 7186; https://doi.org/10.3390/jcm11237186
Submission received: 4 November 2022 / Revised: 26 November 2022 / Accepted: 30 November 2022 / Published: 2 December 2022
(This article belongs to the Section Infectious Diseases)
Browse Figures
Review Reports Versions Notes

Abstract

:
It is not known if the degrees of improvement in oxygenation obtained by CPAP can predict clinical outcomes in patients with COVID-19 pneumonia. This was a retrospective study conducted on patients with severe COVID-19 pneumonia treated with CPAP in three University hospitals in Milan, Italy, from March 2020 to March 2021. Arterial gas analysis was obtained before and 1 h after starting CPAP. CPAP failure included either death in the respiratory units while on CPAP or the need for intubation. Two hundred and eleven patients (mean age 64 years, 74% males) were included. Baseline median PaO2, PaO2/FiO2 ratio (P/F), and the alveolar-arterial (A-a) O2 gradient were 68 (57–83) mmHg, 129 (91–179) mmHg and 310 (177–559) mmHg, respectively. Forty-two (19.9%) patients died in the respiratory units and 51 (24.2%) were intubated. After starting CPAP, PaO2/FiO2 increased by 57 (12–113; p < 0.001) mmHg, and (A-a) O2 was reduced by 68 (−25–250; p < 0.001) mmHg. A substantial overlap of PaO2, P/F, and A-a gradient at baseline and during CPAP was observed in CPAP failures and successes; CPAP-associated improvements in oxygenation in both groups were similar. In conclusion, CPAP-associated improvements in oxygenation do not predict clinical outcomes in patients with severe COVID-19 pneumonia.

1. Introduction

Continuous positive airway pressure (CPAP) has been widely employed for respiratory support in patients with acute hypoxic respiratory failure (hARF) due to pneumonia associated with coronavirus disease 2019 (COVID-19) [1]. Application of positive end expiratory pressure (PEEP) by CPAP can improve oxygenation by increasing functional residual capacity, reducing work of breathing, and recruiting nonaerated alveoli in dependent pulmonary regions [2,3].
As shown by a recent proof of concept study, the extent of changes in oxygenation observed with CPAP in COVID-19 pneumonia is highly variable [4], and the identification of physiological variables reflecting the actual recruitment remains arduous. Moreover, currently, it is not known if the improvement of oxygenation with CPAP may lead to different clinical outcomes in patients with COVID-19 pneumonia. We aimed at investigating if changes in commonly used gas exchange indexes may predict the clinical response to CPAP positioning in patients with COVID-19 pneumonia.

2. Materials and Methods

We conducted a multicenter, retrospective study on patients with COVID-19 pneumonia treated with a helmet CPAP (hCPAP) in the High Dependency Respiratory Unit (HDRU) of L. Sacco University Hospital, San Paolo University Hospital and Ospedale Maggiore Policlinico in Milan, Italy, from March 2020 to March 2021. Inclusion criteria were: (i) laboratory-confirmed SARS-CoV-2 infection; (ii) radiological evidence of new-onset pulmonary infiltrates; (iii) a PaO2/FiO2 ≤ 250 mmHg while on oxygen masks with FiO2 ≥ 50% before starting CPAP; (iv) initiation of CPAP within 24 h after HDRU admission.
Assessment of vitals and arterial blood gas analysis (ABG) was performed within 1 h before and 1 h after starting hCPAP. Biochemistry obtained the same day, as well as hospital length of stay and all-cause in-hospital mortality were registered. An hCPAP was started by the protocol as previously reported [5] and was delivered through high-flow generators (VitalSigns Inc., Totowa, NJ, USA; 90–140 L·min−1; MYO 3133A, Pulmodyne, Indianapolis, IN, USA) by means of a helmet interface (StarMed, Intersurgical S.p.A., Mirandola, Italy), with an adjustable PEEP valve (VitalSigns Inc., Totowa, NJ, USA) [3,5]. CPAP was administered in 4- or 5-h cycles during the morning and afternoon, as well as during night-time. In critical patients who were not able to tolerate the suspension of CPAP, CPAP was administered continuously and enteral nutrition was thus implemented. Adherence to CPAP and occurrence of possible complications (e.g., damage to the helmet or circuit, lymphedema in the arms, or vomiting) were monitored and managed by the nursing and medical staff. Patients with poor adherence to CPAP, or in whom CPAP had to be prematurely suspended due to intolerance, were excluded from this study.
Ceiling treatment and a “do not intubate” order (DNI) were judged by the treating physician and the critical care physician according to standard local operating procedures and the patients’ will, as previously reported [5]. Indications for endotracheal intubation (ETI) were the presence of respiratory distress, hemodynamic instability, respiratory arrest, a reduction of alertness, or unsatisfactory oxygenation (SpO2 < 90% or PaO2 < 60 mmHg) while on hCPAP and an FiO2 > 60%. Differences in PaO2 (ΔPaO2), alveolar-arterial (A-a) O2 gradient (ΔA-a O2), PaO2/FiO2 (ΔPaO2/FiO2), and a modified ROX (mROX) index (partial pressure of oxygen (PaO2) to FiO2 ratio/RR) (ΔmROX) [6] before and after CPAP positioning were investigated as markers of improved oxygenation during CPAP, also possibly reflecting increased alveolar recruitment.
CPAP failure was defined as either death in the HDRU or the need for ETI and subsequent transfer to the intensive care unit [7].
Analyses were performed with IBM SPSS Statistics for Windows, V.23.0 (Armonk, NY, USA). Variables were expressed as the median and interquartile range (IQR) or means and standard deviation (SD), according to their distribution assessed with the Shapiro-Wilk test. t-tests for independent groups, Chi-square, or Mann-Whitney tests were used to compare CPAP successes and failures, as appropriate. A Cox regression analysis was performed to investigate the relationship between changes in PaO2, (A-a) O2 gradient, PaO2/FiO2 ratio, and mROX before and during CPAP, and the risk of CPAP failure; the regression was adjusted for either the severity of pneumonia assessed before starting CPAP, by means of CURB-65 or comorbidities by means of the Charlson Comorbidity Index.
The area under the receiver operating characteristic curve (AUROC) was calculated for ΔPaO2, ΔA-a O2, ΔPaO2/FiO2, and ΔmROX, considering improvement thresholds of 20%, 40%, and 60% from baseline. Tests were two-sided and statistical significance was taken at p < 0.05. Missing data were not computed for statistical analysis.
The study was conducted in accordance with the amended Declaration of Helsinki (2013) and the study was approved by the Ethical Committees of the involved centers (17263/2020). All patients signed informed consent.

3. Results

Two hundred and eleven patients (mean age 64 years, 74% males) were included. Clinical characteristics are reported in Table 1. At baseline, the median PaO2 was 68 (57–83) mmHg, PaO2/FiO2 was 129 (91–179) mmHg, (A-a) O2 was 310 (177–559) mmHg, and mROX was 4.77 (3.04–7.06). On average, after starting CPAP, PaO2, PaO2/FiO2, and mROX increased by 30 (11–68; p < 0.001), 57 (12–113; p < 0.001) mmHg, and 2.5 (0.8–5.3) units, respectively, while (A-a) O2 was reduced by 68 (−25–250; p < 0.001) mmHg and the respiratory rate decreased by 2 (−7.5–2.0; p < 0.001) bpm.
Ninety-three (44%) patients failed hCPAP, of which 42 (19.9%) died in the HDRU, and 51 (24.2%) required intubation. Patients who failed treatment were older, had a higher Charlson comorbidity index and had worse respiratory parameters both before and after starting hCPAP (Table 1). Among the patients who failed CPAP, three developed pneumomediastinum, and one developed pneumothorax (Table 1).
After starting CPAP, the majority of patients showed improvements in all gas exchange indexes, but changes were similar in both successes and failures (Table 1, Figure 1).
Due to the lack of statistical difference in ΔPaO2, ΔPaO2/FiO2, and Δ (A-a) O2 gradient between successes and failures, and also due to the high intragroup dispersion of these parameters, only ΔmROX was included in the Cox regression analysis. No significant correlation was found between improvements in mROX and reduced risk of CPAP failure (Table 2).
AUROC for absolute changes in PaO2, PaO2/FiO2, (A-a) O2, and mROX to predict CPAP success or CPAP failure for any considered improvement threshold were not statistically significant (Figure 2). When considered as percentage changes from the baseline, AUROC for PaO2, PaO2/FiO2, (A-a) O2, and mROX to predict CPAP success or CPAP failure were similarly not significant (data not shown).

4. Discussion

Despite the proven effectiveness of CPAP in treating acute respiratory failure due to COVID-19 [8,9], we found high rates of CPAP failure (either death or need for endotracheal intubation) in our cohort. A possible explanation is the high clinical severity of these patients, who, at baseline, showed very compromised oxygenation and elevated inflammation markers, as well as often having significant comorbidities. Moreover, many of these patients were hospitalized in the early stages of the COVID-19 pandemic before the publication of international studies that improved our understanding of therapeutic strategies in this condition, such as the effectiveness of systemic corticosteroids [10] or the ineffectiveness of azithromycin [11] and hydroxychloroquine [12] in reducing mortality. In fact, it has already been observed that patients hospitalized during the 2020 autumn wave in Italy showed lower mortality rates than those hospitalized during the first SARS-CoV-2 pandemic wave, despite similar rates of intubation [13].
We found that patients who failed CPAP were older, had higher baseline IL-6, D-dimer, CURB-65, and blood urea, as well as worse oxygenation parameters and higher respiratory rate, and were more likely to be affected by ischemic heart disease, COPD, and chronic kidney failure. All these findings are coherent with well-known predictors of unfavorable outcomes in COVID-19 pneumonia [7,14,15,16,17,18,19,20,21].
CPAP may increase oxygenation in COVID-19 pneumonia both by improving the ventilation/perfusion ratio and by reducing the pulmonary shunt, recruiting poorly ventilated lung regions [3]. It has been observed that estimation of lung recruitment by PEEP via the recruitment-to-inflation (R/I) ratio can predict clinical outcomes in the invasive setting [22], but this cannot be applied in patients treated with noninvasive ventilation and CPAP. Moreover, there is high interindividual variability in lung recruitment and a subsequent improvement of oxygenation with CPAP [4,23], which is likely due to the heterogeneity in mechanical characteristics and damage of the lungs in these patients [24,25,26,27].
Since increased lung recruitment can be reflected by an increase in the ventilation/perfusion ratio [27] and a reduction of the alveolar-arterial oxygen gradient [15], it would be expected that the observed variations of these parameters after starting CPAP could help estimate the amount of lung recruitment. This consideration would be valuable for clinicians to assess the response to CPAP, and thus to consider the possible need to modulate PEEP or to proceed to intubation and invasive ventilation, since it is known that delaying intubation may lead to poor prognosis in these patients [28,29]. However, it is already known that CPAP can also decrease the work of breathing and, in some instances, the respiratory rate in patients with increased respiratory efforts, thus proving additionally valuable in patients with COVID-19 pneumonia [2,30,31,32]. Therefore, along with PaO2, PaO2/FiO2 ratio, and (A-a) O2 gradient, we decided to include mROX as a further marker of response to CPAP. In fact, mROX has been proposed as a superior predictor of clinical outcomes compared to the ROX index in patients with acute respiratory failure treated with HFNC [7] or CPAP [33] and reflects the possible reduction of respiratory rate induced by CPAP in patients with respiratory distress.
However, contrary to expectations, we found that short-term improvements in these parameters poorly correlated with clinical outcomes, and thus we were not able to predict a positive or negative response to the application of hCPAP.
Among the parameters we considered as possible markers of lung recruitment after CPAP, we found no significant difference in PaO2, PaO2/FiO2 ratio, and (A-a) O2 gradient between CPAP successes and failures.
Additionally, we found that the absolute increase of mROX, but not its relative increase from the baseline, was greater in successes than in failures; however, punctual increases of mROX were not significantly associated with better outcomes in the respective ROC curve. Moreover, Cox regression showed that improvements in mROX could not predict reduced odds of CPAP failure (Table 2). This contrasts a previous finding that the ROX index may predict better outcomes in severe COVID-19 patients treated with HFNC [34].
There are some possible explanations for this finding. As highlighted by Tobin and collaborators [35], even though the PaO2/FiO2 ratio is widely employed to assess the severity of hypoxemia in pneumonia and ARDS, the accurate measurement of FiO2 could be unfeasible and possibly misleading in a noninvasive setting. The estimation of FiO2 may be further complicated in COVID-19 patients treated with a helmet CPAP, due to the use of filters to prevent the dispersion of viral particles [36] and the need to achieve high oxygen flows in order to provide the target FiO2 [37]. Moreover, the PaO2/FiO2 relationship is not linear, but varies with the degree of ventilation–perfusion inequality and shunt, undermining the reliability of comparing PaO2/FiO2 before and during CPAP [38]. Despite the (A-a) O2 being possibly more accurate than the PaO2/FiO2 ratio in monitoring clinical response to ventilation in CPAP [39], it is still undermined by imprecise measures of FiO2.
Second, CPAP grants respiratory support not only by improving oxygenation but also by improving lung mechanics and reducing the work of breathing [40,41]. Individual degrees of this positive effect would not be reflected by short-term modifications of the gas exchange parameters.
Third, clinical deterioration and mortality in severe COVID-19 pneumonia may be also due to acute complications (e.g., acute pulmonary embolism, kidney failure, or cardiovascular involvement) [1,42], which can’t be predicted by gas exchange parameters after starting CPAP.
Finally, the low odds ratio between improvements in oxygenation and the favorable outcome we found in the logistic regression may be attributable to the limited size of the studied cohort.
The available data suggest that CPAP can reduce the need for intubation in patients with COVID-19 pneumonia compared to other forms of respiratory support, such as high-flow nasal cannula [43]. However, delaying intubation may also increase mortality [44]. Therefore, it would be crucial to promptly identify nonresponders to CPAP, who may need evaluation for undergoing timely intubation to prevent further clinical deterioration. Unfortunately, we found that short-term modifications of respiratory parameters were not helpful for this purpose.
This study has limitations. First, the retrospective nature is a possible source of bias. In fact, some clinical information was not available for all patients, such as the time intercurred between the onset of symptoms and the initiation of CPAP treatment. This could have helped the investigation if the response to the application of PEEP might have been affected by the stage of the disease. Moreover, a chest CT scan performed before and after CPAP positioning would have helped identify the mechanisms underlying the improvement of oxygenation during CPAP, as well as clarify the correlation with alveolar recruitment. Third, the effects of the pharmacological interventions, especially regarding systemic steroids and tocilizumab, have not been taken into account as a confounding factor; however, considering the mechanistic rationale of the study and the acute observation of CPAP effects on gas exchange, we believe that the effect of drug therapy may not have affected by a large extent the response to CPAP. On the contrary, the use of systemic corticosteroids and heparin might have influenced mortality and disease progression [45]. In fact, especially during the first month of the pandemic wave, the usage of systemic steroids was low, which might have influenced the clinical outcome [13]. Lastly, prone positioning may increase lung recruitment and oxygenation and decrease the rate of intubation in COVID-19 pneumonia [46,47,48]. Unfortunately, in our cohort, awake-prone positioning was rarely and heterogeneously applied, which has prevented the evaluation of its possible favorable effects.

5. Conclusions

In conclusion, this study suggests that improvements in oxygenation after starting CPAP can’t be used to inform the clinical outcomes of the individual patient with severe COVID-19 pneumonia. Further prospective studies are necessary to support these observations and help clarify the underlying pathophysiological mechanisms related to the improvement of oxygenation in COVID-19 patients treated with CPAP.

Author Contributions

Conceptualization, S.P., D.R. and P.S.; methodology, S.P., D.R. and P.S.; validation, D.R. and P.S.; formal analysis, S.P., D.R., M.G., F.D., M.M., S.A., S.C., F.B., D.A.C. and P.S.; investigation, S.P., D.R., M.S., M.G., F.D., M.M., S.A., S.C., F.B. and D.A.C.; data curation, S.P., D.R., M.S., M.G., F.D., M.M., S.A., S.C., F.B., D.A.C. and P.S.; writing—original draft, S.P., D.R., M.S., M.G., F.D., M.M., S.A., S.C., F.B., D.A.C. and P.S.; writing—review & editing, S.P., D.R., M.S., M.G., F.D., M.M., S.A., S.C., F.B., D.A.C. and P.S.; supervision, D.R. and P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the involved centers (17263/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings are available from the corresponding author upon reasonable request.

Acknowledgments

The Authors want to thank all the healthcare personnel that participated and provided care for patients with COVID-19 pneumonia during the SARS-CoV-2 pandemic in Italy.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Perkins, G.D.; Ji, C.; Connolly, B.A.; Couper, K.; Lall, R.; Baillie, J.K.; Bradley, J.M.; Dark, P.; Dave, C.; De Soyza, A.; et al. Effect of Noninvasive Respiratory Strategies on Intubation or Mortality Among Patients with Acute Hypoxemic Respiratory Failure and COVID-19: The RECOVERY-RS Randomized Clinical Trial. JAMA 2022, 327, 546–558. [Google Scholar] [CrossRef]
  2. Radovanovic, D.; Rizzi, M.; Pini, S.; Saad, M.; Chiumello, D.A.; Santus, P. Helmet CPAP to Treat Acute Hypoxemic Respiratory Failure in Patients with COVID-19: A Management Strategy Proposal. J. Clin. Med. 2020, 9, 1191. [Google Scholar] [CrossRef] [PubMed]
  3. Radovanovic, D.; Santus, P.; Coppola, S.; Saad, M.; Pini, S.; Giuliani, F.; Mondoni, M.; Chiumello, D.A. Characteristics, outcomes and global trends of respiratory support in patients hospitalized with COVID-19 pneumonia: A scoping review. Minerva Anestesiol. 2021, 87, 915–926. [Google Scholar] [CrossRef] [PubMed]
  4. Amati, F.; Aliberti, S.; Misuraca, S.; Simonetta, E.; Bindo, F.; Vigni, A.; Bassi, L.; Mazzucco, A.; Cara, A.; Blasi, F. Lung Recruitability of COVID-19 Pneumonia in Patients Undergoing Helmet CPAP. Arch. Bronconeumol. 2021, 57, 92–94. [Google Scholar] [CrossRef] [PubMed]
  5. Aliberti, S.; Radovanovic, D.; Billi, F.; Sotgiu, G.; Costanzo, M.; Pilocane, T.; Saderi, L.; Gramegna, A.; Rovellini, A.; Perotto, L.; et al. Helmet CPAP treatment in patients with COVID-19 pneumonia: A multicentre cohort study. Eur. Respir. J. 2020, 56, 2001935. [Google Scholar] [CrossRef]
  6. Radovanovic, D.; Pini, S.; Saad, M.; Perotto, L.; Giuliani, F.; Santus, P. Predictors of weaning from helmet CPAP in patients with COVID-19 pneumonia. Crit. Care 2021, 25, 206. [Google Scholar] [CrossRef]
  7. Santus, P.; Pini, S.; Amati, F.; Saad, M.; Gatti, M.; Mondoni, M.; Tursi, F.; Rizzi, M.; Chiumello, D.A.; Monzani, V.; et al. Predictors of Helmet CPAP Failure in COVID-19 Pneumonia: A Prospective, Multicenter, and Observational Cohort Study. Can. Respir. J. 2022, 2022, 1499690. [Google Scholar] [CrossRef]
  8. Amirfarzan, H.; Cereda, M.; Gaulton, T.; Leissner, K.; Cortegiani, A.; Schumann, R.; Gregoretti, C. Use of Helmet CPAP in COVID-19—A practical review. Pulmonology 2021, 27, 413–422. [Google Scholar] [CrossRef]
  9. Radovanovic, D.; Coppola, S.; Franceschi, E.; Gervasoni, F.; Duscio, E.; Chiumello, D.A.; Santus, P. Mortality and clinical outcomes in patients with COVID-19 pneumonia treated with non-invasive respiratory support: A rapid review. J. Crit. Care 2021, 65, 1–8. [Google Scholar] [CrossRef]
  10. The WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group; Sterne, J.A.C.; Murthy, S.; Diaz, J.V.; Slutsky, A.S.; Villar, J.; Angus, D.C.; Annane, D.; Azevedo, L.C.P.; Berwanger, O.; et al. Association between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients with COVID-19: A Meta-analysis. JAMA 2020, 324, 1330–1341. [Google Scholar] [CrossRef]
  11. Kamel, A.M.; Monem, M.S.A.; Sharaf, N.A.; Magdy, N.; Farid, S.F. Efficacy and safety of azithromycin in COVID-19 patients: A systematic review and meta-analysis of randomized clinical trials. Rev. Med. Virol. 2022, 32, e2258. [Google Scholar] [CrossRef] [PubMed]
  12. Singh, B.; Ryan, H.; Kredo, T.; Chaplin, M.; Fletcher, T. Chloroquine or hydroxychloroquine for prevention and treatment of COVID-19. Cochrane Database Syst. Rev. 2021, 2021, CD013587. [Google Scholar] [CrossRef]
  13. Radovanovic, D.; Pini, S.; Franceschi, E.; Pecis, M.; Airoldi, A.; Rizzi, M.; Santus, P. Characteristics and outcomes in hospitalized COVID-19 patients during the first 28 days of the spring and autumn pandemic waves in Milan: An observational prospective study. Respir. Med. 2021, 178, 106323. [Google Scholar] [CrossRef] [PubMed]
  14. De Vita, N.; Scotti, L.; Cammarota, G.; Racca, F.; Pissaia, C.; Maestrone, C.; Colombo, D.; Olivieri, C.; Della Corte, F.; Barone-Adesi, F.; et al. Predictors of intubation in COVID-19 patients treated with out-of-ICU continuous positive airway pressure. Pulmonology 2022, 28, 173–180. [Google Scholar] [CrossRef]
  15. Ronda, V.E.; Alcaraz, S.R.; Torregrosa, P.R.; Suau, M.G.; Pérez, E.N.; Ramírez, J.M.L.; Andrés, M.; Moreno-Pérez, Ó.; Blanes, A.C.; Gil Carbonell, J.; et al. Application of validated severity scores for pneumonia caused by SARS-CoV-2. Med. Clínica 2021, 157, 99–105. [Google Scholar] [CrossRef]
  16. Arina, P.; Baso, B.; Moro, V.; Patel, H.; Ambler, G.; UCL Critical Care COVID-19 Research Group. Discriminating between CPAP success and failure in COVID-19 patients with severe respiratory failure. Intensive Care Med. 2021, 47, 237–239. [Google Scholar] [CrossRef]
  17. Gerayeli, F.V.; Milne, S.; Cheung, C.; Li, X.; Yang, C.W.T.; Tam, A.; Choi, L.H.; Bae, A.; Sin, D.D. COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis. eClinicalMedicine 2021, 33, 100789. [Google Scholar] [CrossRef]
  18. Soni, M.; Gopalakrishnan, R.; Vaishya, R.; Prabu, P. D-dimer level is a useful predictor for mortality in patients with COVID-19: Analysis of 483 cases. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 2245–2249. [Google Scholar] [CrossRef]
  19. Citu, C.; Burlea, B.; Gorun, F.; Motoc, A.; Gorun, O.M.; Malita, D.; Ratiu, A.; Margan, R.; Grigoras, M.L.; Bratosin, F.; et al. Predictive Value of Blood Coagulation Parameters in Poor Outcomes in COVID-19 Patients: A Retrospective Observational Study in Romania. J. Clin. Med. 2022, 11, 2831. [Google Scholar] [CrossRef]
  20. Brusasco, C.; Corradi, F.; Di Domenico, A.; Raggi, F.; Timossi, G.; Santori, G.; Brusasco, V.; Galliera CPAP-Covid-19 study group. Continuous positive airway pressure in COVID-19 patients with moderate-to-severe respiratory failure. Eur. Respir. J. 2021, 57, 2002524. [Google Scholar] [CrossRef]
  21. Alessandri, F.; Tosi, A.; De Lazzaro, F.; Andreoli, C.; Cicchinelli, A.; Carrieri, C.; Lai, Q.; Pugliese, F.; on behalf of the Policlinico Umberto I COVID-19 Group. Use of CPAP Failure Score to Predict the Risk of Helmet-CPAP Support Failure in COVID-19 Patients: A Retrospective Study. J. Clin. Med. 2022, 11, 2593. [Google Scholar] [CrossRef]
  22. Beloncle, F.M.; Pavlovsky, B.; Desprez, C.; Fage, N.; Olivier, P.-Y.; Asfar, P.; Richard, J.-C.; Mercat, A. Recruitability and effect of PEEP in SARS-Cov-2-associated acute respiratory distress syndrome. Ann. Intensive Care 2020, 10, 55. [Google Scholar] [CrossRef] [PubMed]
  23. Chiumello, D.; Bonifazi, M.; Pozzi, T.; Formenti, P.; Papa, G.F.S.; Zuanetti, G.; Coppola, S. Positive end-expiratory pressure in COVID-19 acute respiratory distress syndrome: The heterogeneous effects. Crit. Care 2021, 25, 431. [Google Scholar] [CrossRef] [PubMed]
  24. Gattinoni, L.; Chiumello, D.; Caironi, P.; Busana, M.; Romitti, F.; Brazzi, L.; Camporota, L. COVID-19 pneumonia: Different respiratory treatments for different phenotypes? Intensive Care Med. 2020, 46, 1099–1102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Lascarrou, J.-B. COVID-19-related ARDS: One disease, two trajectories, and several unanswered questions. Lancet Respir. Med. 2021, 9, 1345–1347. [Google Scholar] [CrossRef] [PubMed]
  26. Cronin, J.N.; Camporota, L.; Formenti, F. Mechanical ventilation in COVID-19: A physiological perspective. Exp. Physiol. 2022, 107, 683–693. [Google Scholar] [CrossRef] [PubMed]
  27. Zerbib, Y.; Lambour, A.; Maizel, J.; Kontar, L.; De Cagny, B.; Soupison, T.; Bradier, T.; Slama, M.; Brault, C. Respiratory effects of lung recruitment maneuvers depend on the recruitment-to-inflation ratio in patients with COVID-19-related acute respiratory distress syndrome. Crit. Care 2022, 26, 12. [Google Scholar] [CrossRef]
  28. Le Terrier, C.; Suh, N.; Wozniak, H.; Boroli, F.; Giudicelli-Bailly, A.; Sangla, F.; Legouis, D.; Bendjelid, K.; Quintard, H.; Pugin, J. Delayed intubation is associated with mortality in patients with severe COVID-19: A single-centre observational study in Switzerland. Anaesth. Crit. Care Pain Med. 2022, 41, 101092. [Google Scholar] [CrossRef] [PubMed]
  29. Karim, H.M.R.; Esquinas, A.M. Success or Failure of High-Flow Nasal Oxygen Therapy: The ROX Index Is Good, but a Modified ROX Index May Be Better. Am. J. Respir. Crit. Care Med. 2019, 200, 116–117. [Google Scholar] [CrossRef]
  30. Petrof, B.J.; Calderini, E.; Gottfried, S.B. Effect of CPAP on respiratory effort and dyspnea during exercise in severe COPD. J. Appl. Physiol. 1990, 69, 179–188. [Google Scholar] [CrossRef]
  31. Marshall, A.; Pittard, M. Use of continuous positive airway pressure (CPAP) in the critically ill—Physiological principles. Aust. Crit. Care 1999, 12, 154–158. [Google Scholar] [CrossRef]
  32. Carteaux, G.; Pons, M.; Morin, F.; Tuffet, S.; Lesimple, A.; Badat, B.; Haudebourg, A.-F.; Perier, F.; Deplante, Y.; Guillaud, C.; et al. Continuous positive airway pressure for respiratory support during COVID-19 pandemic: A frugal approach from bench to bedside. Ann. Intensive Care 2021, 11, 38. [Google Scholar] [CrossRef]
  33. Camous, L.; Pommier, J.-D.; Martino, F.; Tressieres, B.; Demoule, A.; Valette, M. Very late intubation in COVID-19 patients: A forgotten prognosis factor? Crit. Care 2022, 26, 89. [Google Scholar] [CrossRef] [PubMed]
  34. Abroug, F.; Hammouda, Z.; Lahmar, M.; Nouira, W.; Maatouk, S.; Youssef, S.B.; Dachraoui, F.; Brochard, L.; Ouanes-Besbes, L. Early Variation of ROX Index Predicts High-Flow Nasal Cannula Outcome in Awake Subjects with Severe Hypoxemic COVID-19. Respir. Care 2022, 67, 10125. [Google Scholar] [CrossRef] [PubMed]
  35. Tobin, M.J.; Jubran, A.; Laghi, F. PaO2/FIO2ratio: The mismeasure of oxygenation in COVID-19. Eur. Respir. J. 2021, 57, 2100274. [Google Scholar] [CrossRef]
  36. Privitera, D.; Capsoni, N.; Zadek, F.; Vailati, P.; Airoldi, C.; Cozzi, M.; Pierotti, F.; Fumagalli, R.; Bellone, A.; Langer, T. The Effect of Filters on CPAP Delivery by Helmet. Respir. Care 2022, 67, 995–1001. [Google Scholar] [CrossRef]
  37. Lebret, M.; Fresnel, E.; Prieur, G.; Quieffin, J.; Dupuis, J.; Lamia, B.; Combret, Y.; Medrinal, C. High O2 Flow Rates Required to Achieve Acceptable FiO2 in CPAP-Treated Patients with Severe Covid-19: A Clinically Based Bench Study. Arch. Bronconeumol. 2021, 57, 607–610. [Google Scholar] [CrossRef]
  38. Aboab, J.; Louis, B.; Jonson, B.; Brochard, L. Relation between PaO2/FIO2 ratio and FIO2: A mathematical description. Intensive Care Med. 2006, 32, 1494–1497. [Google Scholar] [CrossRef]
  39. Tobin, M.J. Basing Respiratory Management of COVID-19 on Physiological Principles. Am. J. Respir. Crit. Care Med. 2020, 201, 1319–1320. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Katz, J.A.; Marks, J.D. Inspiratory Work with and without Continuous Positive Airway-Pressure in Patients with Acute Respiratory Failure. Anesthesiology 1985, 63, 598–607. [Google Scholar] [CrossRef] [PubMed]
  41. Tobin, M.J. Extubation and the Myth of “Minimal Ventilator Settings”. Am. J. Respir. Crit. Care Med. 2012, 185, 349–350. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Potere, N.; Valeriani, E.; Candeloro, M.; Tana, M.; Porreca, E.; Abbate, A.; Spoto, S.; Rutjes, A.W.S.; Di Nisio, M. Acute complications and mortality in hospitalized patients with coronavirus disease 2019: A systematic review and meta-analysis. Crit. Care 2020, 24, 389. [Google Scholar] [CrossRef]
  43. Grieco, D.L.; Menga, L.S.; Cesarano, M.; Rosà, T.; Spadaro, S.; Bitondo, M.M.; Montomoli, J.; Falò, G.; Tonetti, T.; Cutuli, S.L.; et al. Effect of Helmet Noninvasive Ventilation vs High-Flow Nasal Oxygen on Days Free of Respiratory Support in Patients with COVID-19 and Moderate to Severe Hypoxemic Respiratory Failure: The HENIVOT Randomized Clinical Trial. JAMA 2021, 325, 1731–1743. [Google Scholar] [CrossRef] [PubMed]
  44. Vaschetto, R.; Barone-Adesi, F.; Racca, F.; Pissaia, C.; Maestrone, C.; Colombo, D.; Olivieri, C.; De Vita, N.; Santangelo, E.; Scotti, L.; et al. Outcomes of COVID-19 patients treated with continuous positive airway pressure outside the intensive care unit. ERJ Open Res. 2021, 7, 00541–02020. [Google Scholar] [CrossRef] [PubMed]
  45. Salton, F.; Confalonieri, P.; Meduri, G.U.; Santus, P.; Harari, S.; Scala, R.; Lanini, S.; Vertui, V.; Oggionni, T.; Caminati, A.; et al. Prolonged Low-Dose Methylprednisolone in Patients with Severe COVID-19 Pneumonia. Open Forum Infect. Dis. 2020, 7, ofaa421. [Google Scholar] [CrossRef] [PubMed]
  46. Coppo, A.; Bellani, G.; Winterton, D.; Di Pierro, M.; Soria, A.; Faverio, P.; Cairo, M.; Mori, S.; Messinesi, G.; Contro, E.; et al. Feasibility and physiological effects of prone positioning in non-intubated patients with acute respiratory failure due to COVID-19 (PRON-COVID): A prospective cohort study. Lancet Respir. Med. 2020, 8, 765–774. [Google Scholar] [CrossRef]
  47. Paternoster, G.; Sartini, C.; Pennacchio, E.; Lisanti, F.; Landoni, G.; Cabrini, L. Awake pronation with helmet continuous positive airway pressure for COVID-19 acute respiratory distress syndrome patients outside the ICU: A case series. Med. Intensiv. 2020, 46, 65–71. [Google Scholar] [CrossRef]
  48. Li, J.; Luo, J.; Pavlov, I.; Perez, Y.; Tan, W.; Roca, O.; Tavernier, E.; Kharat, A.; McNicholas, B.; Ibarra-Estrada, M.; et al. Awake prone positioning for non-intubated patients with COVID-19-related acute hypoxaemic respiratory failure: A systematic review and meta-analysis. Lancet Respir. Med. 2022, 10, 573–583. [Google Scholar] [CrossRef]
Jcm 11 07186 g001 550
Figure 1. PaO2, (A-a) O2 gradient, PaO2/FiO2 ratio, and mROX before and during CPAP, and relative differences. Physiological variables are plotted as a function of age to show their dispersed distribution in different patients. Orange circles = CPAP successes; Blue circles = CPAP failures; PaO2 = arterial partial pressure of oxygen; CPAP = continuous positive airway pressure; mROX = PaO2/FiO2/respiratory rate; (A-a) O2 gradient = alveolar-arterial oxygen gradient; FiO2 = fraction of inspired oxygen.
Figure 1. PaO2, (A-a) O2 gradient, PaO2/FiO2 ratio, and mROX before and during CPAP, and relative differences. Physiological variables are plotted as a function of age to show their dispersed distribution in different patients. Orange circles = CPAP successes; Blue circles = CPAP failures; PaO2 = arterial partial pressure of oxygen; CPAP = continuous positive airway pressure; mROX = PaO2/FiO2/respiratory rate; (A-a) O2 gradient = alveolar-arterial oxygen gradient; FiO2 = fraction of inspired oxygen.
Jcm 11 07186 g001
Jcm 11 07186 g002 550
Figure 2. ROC curves for absolute increases in PaO2, (A-a) O2 gradient, PaO2/FiO2 ratio, and mROX before and during CPAP, and relative differences. PaO2 = arterial partial pressure of oxygen; FiO2 = fraction of inspired oxygen; (A-a) O2 gradient = alveolar-arterial oxygen gradient; mROX = PaO2/FiO2/respiratory rate; AUROC = area under the ROC curve.
Figure 2. ROC curves for absolute increases in PaO2, (A-a) O2 gradient, PaO2/FiO2 ratio, and mROX before and during CPAP, and relative differences. PaO2 = arterial partial pressure of oxygen; FiO2 = fraction of inspired oxygen; (A-a) O2 gradient = alveolar-arterial oxygen gradient; mROX = PaO2/FiO2/respiratory rate; AUROC = area under the ROC curve.
Jcm 11 07186 g002
Table
Table 1. Clinical and gas exchange characteristics before and after CPAP positioning in the whole study population and in patients that succeeded or failed respiratory support with CPAP. Values are reported as frequencies or median (IQR), as appropriate. A calculated comparing patients that succeeded and failed CPAP. * p < 0.001 for the difference between baseline value and post CPAP.
Table 1. Clinical and gas exchange characteristics before and after CPAP positioning in the whole study population and in patients that succeeded or failed respiratory support with CPAP. Values are reported as frequencies or median (IQR), as appropriate. A calculated comparing patients that succeeded and failed CPAP. * p < 0.001 for the difference between baseline value and post CPAP.
CharacteristicsEntire Study
Population
(n = 211)		CPAP Success
(a)
(n = 118)		CPAP Failure
(b)
(n = 93)		p-Value
(a vs. b)
Age, years64 (55–74)61 (51–72)67 (60–76)<0.001
Males, n (%)155 (73.5)88 (74.6)67 (72)0.398
Past medical history
Charlson Comorbidity Index, score3 (1–4)2 (1–4)2 (3–5)<0.001
Hypertension, n (%)94 (44.5)53 (44.9)41 (44.1)0.508
Diabetes mellitus, n (%)48 (22.7)23 (19.5)25 (26.9)0.134
Ischemic heart disease, n (%)36 (17.1)12 (10.2)24 (25.8)0.002
Arrhythmia, n (%)17 (8.5)8 (7.2)9 (10.1)0.315
SmokeActive, n (%)7 (3.4)5 (4.4)2 (2.2)0.613
Ex, n (%)34 (16.6)17 (14.9)17 (18.7)
Never, n (%)164 (80)92 (80.7)72 (79.1)
COPD, n (%)15 (7.1)4 (3.4)11 (11.8)0.018
Asthma, n (%)13 (6.2)8 (6.8)5 (5.4)0.452
CKD, n (%)14 (6.6)3 (2.5)11 (11.8)0.008
Immune depression, n (%)11 (5.5)6 (5.4)5 (5.6)0.593
Clinical variables before starting CPAP
Leucocytes, ×103/L8.84 (2.07–55)7.28 (5.60–10.31)9 (5.81–11.28)0.168
Urea, mg/dL38 (28–55)37 (26–50)45.5 (30–65.5)0.004
IL 6, pg/mL56 (22–134)45 (14–76)114.5 (45.75–254.75)<0.001
CRP, mg/L24.08 (11.63–93.9)25 (11.23–81.5)21.82 (12.45–120)0.457
Platelets, ×103/L225.5 (169–302.75)242 (175–318.5)207 (144–284)0.062
D-Dimer, µg/L FEU929.5 (593–1651.25)803 (568–1281)1105 (668–2491)0.006
CURB 652 (1–2)1 (0–2)2 (1–3)<0.001
FiO2, %60 (40–100)60 (40–60)60 (40–100)0.027
pH7.48 (7.45–7.51)7.48 (7.45–7.5)7.48 (7.46–7.51)0.308
PaO2, mmHg68 (57–83)72.5 (60–87)60 (49.5–72.5)<0.001
PaCO2, mmHg33 (30–37)34 (30–37.2)32 (28–35)0.006
Respiratory rate, bpm28 (24–32)27 (22–30)30 (25–36)<0.001
PaO2/FiO2, mmHg128 (91–179)141.8 (109.6–194.6)118.23 (61–165.5)<0.001
(A-a) O2, mmHg310 (177–559)280.9 (176.7–333.7)337.8 (177.95–608.5)0.001
mROX4.77 (3.04–7.06)5.7 (4–7.6)3.7 (2–5.7)<0.001
Clinical variables after starting CPAP
FiO2, %60 (50–60)50 (50–60)60 (50–65)<0.001
PEEP, cmH2O10 (7.5–10)10 (7.5–10)10 (7.5–12)0.088
pH7.47 (7.44–7.49)7.46 (7.44–7.49)7.47 (7.43–7.49)0.950
PaO2, mmHg100 (79–141)107.5 (86–145.5)85 (68–134.5)0.001
PaCO2, mmHg36 (32–39)36 (34–40)34 (31–39)0.026
PaO2/FiO2, mmHg195 (131.7–256.7)207.5 (165.4–265.8)145 (110–242.7)<0.001
(A-a) O2, mmHg240 (188–308)222 (160.8–274.2)285.3 (212.8–326.8)<0.001
Respiratory rate, bpm24 (22–28)24 (21–28)26 (23–30)0.007
mROX7.4 (5.1–10.7)8.3 (6.5–11.3)6.0 (3.7–9.3)<0.001
Differences compared with baseline
ΔPaO2, mmHg30 (11–68) *32 (11–62)29 (8.5–75)0.976
ΔPaO2, % increase from baseline48.3 (13.8–104.8)46.3 (13.4–90)50.7 (14.6–128.4)0.407
ΔPaO2/FiO2, mmHg57 (12–113.3) *60.7 (22–108.6)51.1 (−4.9–142.7)0.307
ΔPaO2/FiO2, % increase from baseline50.4 (9.1–104.9)43.4 (13.4–89.6)66.2 (−4.1–136.6)0.393
Δ(A-a) O2, mmHg68 (−25–250) *55 (−7–136)91 (−68–304)0.211
Δ(A-a) O2, % decrease from baseline20.7 (−10.6–49.5)18.2 (−4.4–48.46)27 (−38.5–51)0.742
ΔRespiratory rate, bpm−2 (−7.5–2.0) *−2.0 (−6.0–3.7)−3.0 (−10.0–2.0)0.167
ΔRespiratory rate, % decrease from baseline8.2 (−10–24.3)6.7 (−12.2–21.4)10 (−6.3–27.3)0.241
ΔmROX2.5 (0.8–5.3) *2.7 (1.0–5.4)2.1 (0.5–5.3)<0.001
ΔmROX, % increase from baseline59.1 (13.8–133.6)53.5 (15.3–111.1)83 (10–210)0.212
Clinical outcomes
CPAP duration, days6.0 (3.5–10)6.5 (5.0–12.0)4.0 (3.0–7.2)<0.001
Hospital stay, days15.0 (10–23)16 (13–24)8.0 (5.0–20)<0.001
Pneumomediastinum, n (%)3 (1.4)0 (0)3 (3.2)0.084
Pneumothorax, n (%)1 (0.5)0 (0)1 (1.1)0.441
Intubated, n (%)51 (24.2)-51 (54.8)n/a
Died in HDRU, n (%)42 (19.9)-42 (45.2)n/a
Variables were expressed as the median and interquartile range (IQR) or means and standard deviation (SD), according to their distribution assessed with the Shapiro-Wilk test. CPAP = continuous positive airway pressure; COPD = chronic obstructive pulmonary disease; CKD = chronic kidney disease; IL-6 = interleukin 6; CRP = C reactive protein (upper limit of normal 10 mg/L); FEU = fibrinogen equivalent units; CURB-65 = Confusion, blood Urea nitrogen, Respiratory rate, Blood pressure, and age ≥ 65 score; PaO2 = arterial partial pressure of oxygen; PaCO2 = arterial partial pressure of carbon dioxide; FiO2 = fraction of inspired oxygen; A-a O2 gradient = alveolar-arterial oxygen gradient; mROX = PaO2/FiO2/respiratory rate; HDRU = high dependency respiratory unit.
Table
Table 2. Association between changes in mROX before and during CPAP, and risk of CPAP failure. CPAP = continuous positive airway pressure; mROX = PaO2/FiO2/respiratory rate.
Table 2. Association between changes in mROX before and during CPAP, and risk of CPAP failure. CPAP = continuous positive airway pressure; mROX = PaO2/FiO2/respiratory rate.
Cox Regression between ΔmROX and CPAP FailureAdjusted OR95% C.I.p-Value
Not adjusted0.9630.904–1.0260.239
Adjusted for CURB-650.9740.908–1.0460.474
Adjusted for Charlson Comorbidity Index0.9890.925–1.0580.755
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pini, S.; Radovanovic, D.; Saad, M.; Gatti, M.; Danzo, F.; Mondoni, M.; Aliberti, S.; Centanni, S.; Blasi, F.; Chiumello, D.A.; et al. Acute Improvements of Oxygenation with Cpap and Clinical Outcomes in Severe COVID-19 Pneumonia: A Multicenter, Retrospective Study. J. Clin. Med. 2022, 11, 7186. https://doi.org/10.3390/jcm11237186

AMA Style

Pini S, Radovanovic D, Saad M, Gatti M, Danzo F, Mondoni M, Aliberti S, Centanni S, Blasi F, Chiumello DA, et al. Acute Improvements of Oxygenation with Cpap and Clinical Outcomes in Severe COVID-19 Pneumonia: A Multicenter, Retrospective Study. Journal of Clinical Medicine. 2022; 11(23):7186. https://doi.org/10.3390/jcm11237186

Chicago/Turabian Style

Pini, Stefano, Dejan Radovanovic, Marina Saad, Marina Gatti, Fiammetta Danzo, Michele Mondoni, Stefano Aliberti, Stefano Centanni, Francesco Blasi, Davide Alberto Chiumello, and et al. 2022. "Acute Improvements of Oxygenation with Cpap and Clinical Outcomes in Severe COVID-19 Pneumonia: A Multicenter, Retrospective Study" Journal of Clinical Medicine 11, no. 23: 7186. https://doi.org/10.3390/jcm11237186

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop