Next Article in Journal
Complete Resolution of Central Soft Drusen without Geographic Atrophy or Choroidal Neovascularization
Previous Article in Journal
Reduction in the Duration of Postoperative Fever during the COVID-19 Pandemic in Orthopedic and Traumatic Surgery Due to PPE and Cautions
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support

1
Institute of Anesthesiology and Intensive Care, Padua University Hospital, 13 Via Gallucci, 35121 Padua, Italy
2
Institute of Radiology, Padua University Hospital, 2 Via Nicolò Giustiniani, 35128 Padua, Italy
3
Institute of Anesthesiology and Intensive Care, Department of Medicine, University of Padua, 2 Via Nicolò Giustiniani, 35128 Padua, Italy
4
Pediatric Radiology, Padua University Hospital, 2 Via Nicolò Giustiniani, 35128 Padua, Italy
5
Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, 23 Spitalgasse, 1090 Vienna, Austria
6
Internal Medicine, Department of Medicine, University of Padua, 2 Via Nicolò Giustiniani, 35128 Padua, Italy
7
Respiratory Pathophysiology Division, Department of Cardio-Thoracic, Vascular Sciences and Public Health, University of Padua, 2 Via Nicolò Giustiniani, 35128 Padua, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Clin. Med. 2022, 11(6), 1636; https://doi.org/10.3390/jcm11061636
Submission received: 20 February 2022 / Revised: 12 March 2022 / Accepted: 14 March 2022 / Published: 16 March 2022

Abstract

:
Forms of noninvasive respiratory support (NIRS) have been widely used to avoid endotracheal intubation in patients with coronavirus disease-19 (COVID-19). However, inappropriate prolongation of NIRS may delay endotracheal intubation and worsen patient outcomes. The aim of this retrospective study was to assess whether the CARE score, a chest X-ray score previously validated in COVID-19 patients, may predict the need for endotracheal intubation and escalation of respiratory support in COVID-19 patients requiring NIRS. From December 2020 to May 2021, we included 142 patients receiving NIRS who had a first chest X-ray available at NIRS initiation and a second one after 48–72 h. In 94 (66%) patients, the level of respiratory support was increased, while endotracheal intubation was required in 83 (58%) patients. The CARE score at NIRS initiation was not predictive of the need for endotracheal intubation (odds ratio (OR) 1.01, 95% confidence interval (CI) 0.96–1.06) or escalation of treatment (OR 1.01, 95% CI 0.96–1.07). In conclusion, chest X-ray severity, as assessed by the CARE score, did not allow predicting endotracheal intubation or escalation of respiratory support in COVID-19 patients undergoing NIRS.

1. Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may lead to the development of coronavirus disease-19 (COVID-19). In 5–8% of patients, COVID-19 causes acute hypoxemic respiratory failure (hARF) requiring intensive care unit (ICU) admission [1,2,3,4,5]. Hypoxemic COVID-19 patients often require forms of noninvasive respiratory support (NIRS), i.e., high-flow nasal oxygen (HFNO), continuous positive airway pressure (CPAP), or noninvasive ventilation (NIV), to avoid endotracheal intubation [6,7]. Undue delays in endotracheal intubation may adversely affect patient outcomes and increase mortality because of patient self-inflicted lung injury (P-SILI), i.e., a form of lung injury that depends on the patient’s high respiratory efforts, generating excessively high transpulmonary pressure [8]. The availability of reliable predictors of NIRS failure is therefore of utmost importance to guide the decision to intubate COVID-19 patients receiving NIRS.
Chest X-ray and computed tomography (CT)-based scores quantifying disease severity have been developed and their prognostic accuracy investigated, demonstrating in retrospective studies that they can help predict mortality [9,10,11,12,13,14]. In particular, the CARE score, which is based on the assessment of ground-glass opacity and consolidation at the chest X-ray, was shown to be a predictor of hospital mortality [11]. However, no study has insofar systematically assessed the performance of chest imaging in predicting the risk of endotracheal intubation in COVID-19 hypoxemic patients undergoing NIRS.
In the present study, we aimed to ascertain whether or not the type and extent of chest X-ray abnormalities, as assessed by the CARE score, may help predict the need for endotracheal intubation (primary endpoint) and escalation of respiratory support, the duration of invasive mechanical ventilation, and hospital mortality (secondary endpoints) in patients with hARF secondary to COVID-19.

2. Materials and Methods

2.1. Patients and Measurements

We included all patients referred to the University Hospital of Padua (Italy), from December 2020 to May 2021, for hARF secondary to COVID-19, who underwent NIRS and had a first chest X-ray available immediately prior to NIRS initiation (first chest X-ray) and a second one after 48–72 h (second chest X-ray) of NIRS. Patients were enrolled from one ICU and two high-dependency units. We excluded patients receiving conventional oxygen therapy (e.g., nasal prongs, simple face masks, venturi mask, non-rebreather mask) as the maximum level of respiratory support, those intubated without receiving NIRS, and those for whom NIRS was the ceiling of treatment. The escalation of respiratory support was defined as any increase in the level of support, i.e., from HFNO to CPAP/NIV or from CPAP to NIV. Among the ICU patients, patients receiving NIRS out of the ICU were excluded. The indications for NIRS initiation, escalation of respiratory support, and endotracheal intubation followed regional guidelines [15]. The study was conducted in accordance with the Declaration of Helsinki and approved by the institutional review board (protocol 183n/AO/21). Patients who survived gave their informed consent for inclusion, whereas those who died were included with a waiver of consent.
Patients were analyzed in two separate groups, according to whether or not they required endotracheal intubation because of NIRS failure. The clinical and laboratory variables collected at NIRS initiation and the outcome variables are described in Table S1. Arterial blood gas analysis was always obtained right before NIRS initiation.
The CARE score was independently calculated by two radiologists who are experts in thoracic imaging, blinded to patients’ outcomes, and the final score was agreed upon after consensus. The composition and computation of the CARE score have been previously described [11]. Briefly, each lung was divided into three areas (upper, middle, and lower), and a four-grade score separately assessing the extent of ground-glass opacity and consolidation was calculated. In each area, the ground-glass sub-score was graded from 0 to 3 and the consolidation sub-score from 4 to 6. The global CARE score was derived from the sum of the two sub-scores up to a maximum value of 36 [11]. The CARE score was calculated for the chest X-ray prior to NIRS initiation (first CARE score) and the chest X-ray at 48–72 h (second CARE score), and the delta CARE score was computed as the difference between the first and second CARE scores.
The reporting of the present study followed the “Strengthening the reporting of observational studies in epidemiology” (STROBE) statement guidelines (Table S2) [16].

2.2. Statistical Analysis

Baseline variables (i.e., demographic characteristics, comorbidities, and laboratory findings at enrollment) and outcome variables are shown in Table S1. Categorical variables are presented as absolute numbers (n) and percentages (%). For continuous variables, the median and interquartile range (IQR) are reported. Fisher’s exact test was applied for categorical variables, whereas Wilcoxon’s rank-sum test was used for continuous variables. Wilcoxon’s signed-rank test was used to investigate if the CARE score changed between the first and the second chest X-ray.
The association between the first CARE score and the need for endotracheal intubation was investigated with univariable logistic regression. Multivariable logistic regression was used to assess potential confounders among the baseline variables described in Table S1. Variables found to be significantly associated with the outcome (p < 0.05) were entered into the multivariable model. Multicollinearity was defined as a variance inflation factor (VIF) = 1/(1 − R2) greater than or equal to 2.5, where R is the percentage of variance in the individual covariates, and variables characterized by multicollinearity were sequentially removed starting from the variable associated with the highest VIF [17].
As secondary outcomes, multivariable logistic and linear regressions were applied to assess the association between the first CARE score and the escalation of respiratory support, invasive mechanical ventilation duration, and hospital mortality, as appropriate.
All statistical tests were two-tailed, and statistical significance was defined as p < 0.05. Statistical analysis was performed using SPSS version 27.0 (SPSS Software, IBM Corp., Armonk, NY, USA) and R version 4.1.1 (R Project for Statistical Computing, Vienna, Austria).

3. Results

Of 386 patients admitted in the study period, 142 patients met the inclusion criteria and were analyzed (Figure 1). Patients’ baseline characteristics are reported in Table 1. Patients were 69 (58–75) years old on average, and 44 (31%) patients were female. Patients were admitted after 6 (4–9) days from symptom onset. The Charlson comorbidity index was 3 (2–5). The sequential organ failure assessment (SOFA) score was 3 (2–4), whereas the respiratory component of SOFA was 2 (2–2).
In 83 (58%) patients, NIRS failed and endotracheal intubation was required. As reported in Table 1, we observed a significantly longer duration of symptoms (7 (4–10) vs. 6 (3–8) days, p = 0.04), higher SOFA scores (3 (2–4) vs. 2 (2–3), p < 0.01), C-reactive protein (CRP) (113 (62–180) vs. 90 (41–123) mg/L, p = 0.04), leukocyte counts (7.81 (5.98–11.26) vs. 6.84 (3.32–9.60) × 109 cells/L, p = 0.03), and interleukin-6 (67 (39–165) vs. 51 (26–99) pg/mL, p = 0.03), and a lower arterial partial pressure of oxygen-to-inspired oxygen fraction ratio (PaO2/FiO2) (104 (78–134) vs. 148 (105–177) mmHg, p < 0.01) at NIRS initiation in the group of patients who were intubated.
Patients’ outcomes are described in Table 2. In 35 (25%) patients, the level of respiratory support was increased. In the group of patients necessitating intubation, prone positioning was overall more frequent (71 [86%] vs. 14 [24%] patients, p < 0.01), hospital length of stay was longer (29 (21–41) vs. 16 (12–22) days, p < 0.01), and hospital mortality was greater (19 [23%] vs. 1 [2%], p < 0.01).
Chest X-rays from two representative patients with similar first CARE scores are presented in Figure 2. The first CARE score was 9 (6–14) and ranged from 0 to 35, while the second decreased to 8 (4–14, p = 0.04) and ranged from 0 to 27 (Table 3). The reduction in the CARE score between the first and the second chest X-ray achieved statistical significance for patients receiving intubation, but not for those receiving NIRS (Table 3).
In the univariable logistic regression, the first CARE score (odds ratio (OR) 1.01, 95% confidence interval (CI) 0.96–1.06) was predictive of endotracheal intubation. In the multivariable logistic regression, a lower Charlson comorbidity index (OR 0.79, 95% CI 0.65–0.95, p = 0.01) and PaO2/FiO2 (OR 0.99, 95% CI 0.98–1.00, p = 0.01) and a greater CRP (OR 1.01, 95% CI 1.00–1.01, p = 0.03) were the only independent predictors of endotracheal intubation (Table 4).
A lower PaO2/FiO2 (OR 0.99, 95% CI 0.99–1.00, p < 0.01) was the only independent predictor of the escalation of respiratory support (Table S3). The SOFA score was the only predictor of the duration of invasive mechanical ventilation (estimate 1.66, 95% CI 0.16–3.16, p = 0.03) (Table S4). Age and the occurrence of endotracheal intubation were the only independent predictors of hospital mortality in the multivariable logistic regression analysis (OR 1.14, 95% CI 1.03–1.26, p = 0.01, and OR 33.50, 95% CI 3.49–122.00, p < 0.01) (Tables S5 and S6).

4. Discussion

We found that the CARE score did not predict the need for endotracheal intubation and escalation of respiratory support in patients receiving NIRS for aHRF secondary to COVID-19. In addition, the CARE score was not predictive of the duration of invasive mechanical ventilation or of hospital mortality.
In COVID-19 patients, NIRS has been proposed to avoid intubation and provide early post-extubation respiratory support [6,7,18]. However, the potential risk of P-SILI may outweigh the benefits of NIRS (e.g., reduced risk of ventilator-associated pneumonia, sedation-related adverse effects) [19,20].
Several clinical risk stratification tools have been developed to predict COVID-19 progression, thereby identifying the required intensity of treatment and the proper setting of care [21,22,23,24]. Radiological techniques assessing COVID-19-related lung abnormalities and based on chest X-rays [9,10,11,12], CT scans [13,14], and lung ultrasounds [25,26,27,28] have been proposed to predict mortality. In particular, the CARE score demonstrated good accuracy in predicting hospital mortality in a previous study including 175 patients overall (44 at home from the emergency department, 95 treated only in the ward, and 36 transferred from the ward to the ICU) [11].
Our results are in keeping with the study of Sargent et al., who found CXR to not be predictive of a composite outcome including intubation or mortality in 58 patients receiving CPAP [10], and consistent with the findings of Bellani et al., who reported greater CRP and a lower PaO2/FiO2 to be associated with an increased risk of intubation or death in 909 patients receiving CPAP or NIV outside the ICU [29]. Different from our results, in a retrospective cohort study including 140 patients, Xiao et al. observed that the severity of chest X-rays at admission independently predicted the time to intubation in COVID-19 patients admitted to the medical ward [30]. However, only 13% of those patients were receiving supplemental oxygen at the time of the chest X-ray, which suggests those patients definitely had less severe lung involvement.
Differences in patient populations may explain these findings. Patients who require NIRS are, in principle, more severely affected by COVID-19 pneumonia, as characterized by extensive ground-glass opacities and/or consolidations, leading, in general, to more uniformly severe radiological scores. Therefore, the choice to proceed to endotracheal intubation is more likely to depend on factors other than radiological characteristics, such as patients’ clinical status and gas exchange derangements. Additionally worth mentioning is that the median 24 h CARE score in the present study was 9, much higher than the median value of 3 observed in the previous study of Giraudo and colleagues, performed on a population of patients with variable severity [11]. Future studies may apply artificial intelligence techniques to better discriminate different radiological severities on CXR [31].
We found NIRS failure to be an independent risk factor for mortality. This is in keeping with the results of Grasselli et al., who observed that ICU patients who failed NIV had a significantly lower chance of survival as compared to those who did not [32].
Our study has some limitations. First, we cannot exclude selection biases consequent to the retrospective data collection and doubts about generalizability because of the single-center design. Second, the results obtained with a score based on chest X-rays may not apply to CT scans and lung ultrasounds, which might be necessary to define the severity of lung lesions in this patient population [13,14,25,27]. Nonetheless, plain film X-ray is the standard technique in critically ill COVID-19 patients [33]. Third, the interobserver agreement was not evaluated. Nonetheless, the CARE score has already been validated [11,34,35,36], and the chest X-ray analysis was performed by two independent radiologists who were blinded to each other and to patients’ clinical status. Last, we cannot exclude that having included patients infected with different viral variants impacted our findings. Indeed, a recent CT-based study demonstrated a more consolidative pattern in infections due to the strain isolated in South Africa than in those consequent to the European strain [37]. Nonetheless, we are led to believe that the simultaneous occurrence of different variants was unlikely and, in any case, limited in our population, since the present study took place in a relatively short interval of time (6 months).
In conclusion, we observed that a validated chest X-ray severity score did not predict the need for endotracheal intubation and escalation of respiratory support in COVID-19 patients undergoing noninvasive respiratory support.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm11061636/s1, Table S1. Baseline and outcome variables, Table S2. Strengthening the reporting of observational studies in epidemiology checklist, Table S3. Logistic regression for the escalation of respiratory support, Table S4. Linear regression for endotracheal intubation duration, Table S5. Comparison between hospital survivors and non-survivors, Table S6. Logistic regression for hospital mortality.

Author Contributions

Concept and design: T.P., C.G., N.S. and P.N.; acquisition, analysis and interpretation of the data: T.P., C.G., G.F., M.W., M.D.P., M.T., D.G., S.C., F.L., S.L., A.B., A.D.C., L.P., M.R., A.V., R.V., N.S. and P.N.; drafting of the manuscript: T.P., C.G., G.F., M.W., M.D.P., M.T., D.G., S.C., F.L., S.L., A.B., A.D.C., L.P., N.S. and P.N.; critical revision of the manuscript for important intellectual content: T.P., G.F., M.W., M.D.P., M.T., D.G., S.C., F.L., S.L., C.G., A.B., A.D.C., L.P., M.R., A.V., R.V., N.S. and P.N. All authors have approved the submitted version and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board, Comitato Etico per la Sperimentazione Clinica della Provincia di Padova, Azienda Ospedale Università di Padova, protocol 183n/AO/21.

Informed Consent Statement

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

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We feel indebted to all personnel working in the ICU and the wards of the Padua University Hospital who made this work possible.

Conflicts of Interest

P.N.’s research lab received grants/research equipment from Draeger, Intersurgical SPA and Gilead. P.N. receives royalties from Intersurgical SPA for the Helmet Next invention. He has also received speaking fees from Getinge, Intersurgical SPA, Gilead, MSD, Draeger and Medicair. P.N. has no conflicts of interest to declare in relation to this manuscript. The other authors have no competing interests to declare.

References

  1. Guan, W.; Ni, Z.; Hu, Y.; Liang, W.; Ou, C.; He, J.; Liu, L.; Shan, H.; Lei, C.; Hui, D.S.C.; et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  2. Wu, Z.; McGoogan, J.M. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases from the Chinese Center for Disease Control and Prevention. JAMA 2020, 323, 1239. [Google Scholar] [CrossRef] [PubMed]
  3. Richardson, S.; Hirsch, J.S.; Narasimhan, M.; Crawford, J.M.; McGinn, T.; Davidson, K.W.; The Northwell COVID-19 Research Consortium; Barnaby, D.P.; Becker, L.B.; Chelico, J.D.; et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized with COVID-19 in the New York City Area. JAMA 2020, 323, 2052. [Google Scholar] [CrossRef] [PubMed]
  4. Vahidy, F.S.; Drews, A.L.; Masud, F.N.; Schwartz, R.L.; Askary, B.B.; Boom, M.L.; Phillips, R.A. Characteristics and Outcomes of COVID-19 Patients during Initial Peak and Resurgence in the Houston Metropolitan Area. JAMA 2020, 324, 998. [Google Scholar] [CrossRef] [PubMed]
  5. Grasselli, G.; Zangrillo, A.; Zanella, A.; Antonelli, M.; Cabrini, L.; Castelli, A.; Cereda, D.; Coluccello, A.; Foti, G.; Fumagalli, R.; et al. Baseline Characteristics and Outcomes of 1591 Patients Infected with SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA 2020, 323, 1574. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. 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]
  7. Boscolo, A.; Pasin, L.; Sella, N.; Pretto, C.; Tocco, M.; Tamburini, E.; Rosi, P.; Polati, E.; Donadello, K.; Gottin, L.; et al. Outcomes of COVID-19 Patients Intubated after Failure of Non-Invasive Ventilation: A Multicenter Observational Study. Sci. Rep. 2021, 11, 17730. [Google Scholar] [CrossRef] [PubMed]
  8. Brochard, L.; Slutsky, A.; Pesenti, A. Mechanical Ventilation to Minimize Progression of Lung Injury in Acute Respiratory Failure. Am. J. Respir. Crit. Care Med. 2017, 195, 438–442. [Google Scholar] [CrossRef] [PubMed]
  9. Reeves, R.A.; Pomeranz, C.; Gomella, A.A.; Gulati, A.; Metra, B.; Hage, A.N.; Lange, S.; Parekh, M.; Donuru, A.; Lakhani, P.; et al. Performance of a Severity Score on Admission Chest Radiography in Predicting Clinical Outcomes in Hospitalized Patients with Coronavirus Disease (COVID-19). Am. J. Roentgenol. 2021, 217, 623–632. [Google Scholar] [CrossRef] [PubMed]
  10. Sargent, W.; Ali, S.; Kukran, S.; Harvie, M.; Soin, S. The Prognostic Value of Chest X-Ray in Patients with COVID-19 on Admission and When Starting CPAP. Clin. Med. 2021, 21, e14–e19. [Google Scholar] [CrossRef]
  11. Giraudo, C.; Cavaliere, A.; Fichera, G.; Weber, M.; Motta, R.; Pelloso, M.; Tosato, F.; Lupi, A.; Calabrese, F.; Carretta, G.; et al. Validation of a Composed COVID-19 Chest Radiography Score: The Care Project. ERJ Open Res. 2020, 6, 00359-02020. [Google Scholar] [CrossRef] [PubMed]
  12. Balbi, M.; Conti, C.; Imeri, G.; Caroli, A.; Surace, A.; Corsi, A.; Mercanzin, E.; Arrigoni, A.; Villa, G.; Di Marco, F.; et al. Post-Discharge Chest CT Findings and Pulmonary Function Tests in Severe COVID-19 Patients. Eur. J. Radiol. 2021, 138, 109676. [Google Scholar] [CrossRef] [PubMed]
  13. Zhou, S.; Chen, C.; Hu, Y.; Lv, W.; Ai, T.; Xia, L. Chest CT Imaging Features and Severity Scores as Biomarkers for Prognostic Prediction in Patients with COVID-19. Ann. Transl. Med. 2020, 8, 1449. [Google Scholar] [CrossRef] [PubMed]
  14. Francone, M.; Iafrate, F.; Masci, G.M.; Coco, S.; Cilia, F.; Manganaro, L.; Panebianco, V.; Andreoli, C.; Colaiacomo, M.C.; Zingaropoli, M.A.; et al. Chest CT Score in COVID-19 Patients: Correlation with Disease Severity and Short-Term Prognosis. Eur. Radiol. 2020, 30, 6808–6817. [Google Scholar] [CrossRef] [PubMed]
  15. Pasin, L.; Sella, N.; Correale, C.; Boscolo, A.; Rosi, P.; Saia, M.; Mantoan, D.; Navalesi, P. Regional COVID-19 Network for Coordination of SARS-CoV-2 Outbreak in Veneto, Italy. J. Cardiothorac. Vasc. Anesth. 2020, 34, 2341–2345. [Google Scholar] [CrossRef] [PubMed]
  16. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Lancet 2007, 370, 1453–1457. [Google Scholar] [CrossRef]
  17. Johnston, R.; Jones, K.; Manley, D. Confounding and Collinearity in Regression Analysis: A Cautionary Tale and an Alternative Procedure, Illustrated by Studies of British Voting Behaviour. Qual. Quant. 2018, 52, 1957–1976. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Cammarota, G.; Vaschetto, R.; Azzolina, D.; De Vita, N.; Olivieri, C.; Ronco, C.; Longhini, F.; Bruni, A.; Colombo, D.; Pissaia, C.; et al. Early Extubation with Immediate Non-Invasive Ventilation versus Standard Weaning in Intubated Patients for Coronavirus Disease 2019: A Retrospective Multicenter Study. Sci. Rep. 2021, 11, 13418. [Google Scholar] [CrossRef] [PubMed]
  19. Scala, R.; Pisani, L. Noninvasive Ventilation in Acute Respiratory Failure: Which Recipe for Success? Eur. Respir. Rev. 2018, 27, 180029. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Akoumianaki, E.; Ischaki, E.; Karagiannis, K.; Sigala, I.; Zakyn-thinos, S. The Role of Noninvasive Respiratory Management in Patients with Severe COVID-19 Pneumonia. JPM 2021, 11, 884. [Google Scholar] [CrossRef] [PubMed]
  21. Knight, S.R.; Ho, A.; Pius, R.; Buchan, I.; Carson, G.; Drake, T.M.; Dunning, J.; Fairfield, C.J.; Gamble, C.; Green, C.A.; et al. Risk Stratification of Patients Admitted to Hospital with Covid-19 Using the ISARIC WHO Clinical Characterisation Protocol: Development and Validation of the 4C Mortality Score. BMJ 2020, 370, m3339. [Google Scholar] [CrossRef] [PubMed]
  22. Liang, W.; Liang, H.; Ou, L.; Chen, B.; Chen, A.; Li, C.; Li, Y.; Guan, W.; Sang, L.; Lu, J.; et al. Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients with COVID-19. JAMA Int. Med. 2020, 180, 1081. [Google Scholar] [CrossRef] [PubMed]
  23. King, J.T.; Yoon, J.S.; Rentsch, C.T.; Tate, J.P.; Park, L.S.; Kidwai-Khan, F.; Skanderson, M.; Hauser, R.G.; Jacobson, D.A.; Erdos, J.; et al. Development and Validation of a 30-Day Mortality Index Based on Pre-Existing Medical Administrative Data from 13,323 COVID-19 Patients: The Veterans Health Administration COVID-19 (VACO) Index. PLoS ONE 2020, 15, e0241825. [Google Scholar] [CrossRef] [PubMed]
  24. Ji, D.; Zhang, D.; Xu, J.; Chen, Z.; Yang, T.; Zhao, P.; Chen, G.; Cheng, G.; Wang, Y.; Bi, J.; et al. Prediction for Progression Risk in Patients with COVID-19 Pneumonia: The CALL Score. Clin. Infect. Dis. 2020, 71, 1393–1399. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Ji, L.; Cao, C.; Gao, Y.; Zhang, W.; Xie, Y.; Duan, Y.; Kong, S.; You, M.; Ma, R.; Jiang, L.; et al. Prognostic Value of Bedside Lung Ultrasound Score in Patients with COVID-19. Crit. Care 2020, 24, 700. [Google Scholar] [CrossRef] [PubMed]
  26. Bonadia, N.; Carnicelli, A.; Piano, A.; Buonsenso, D.; Gilardi, E.; Kadhim, C.; Torelli, E.; Petrucci, M.; Di Maurizio, L.; Biasucci, D.G.; et al. Lung Ultrasound Findings Are Associated with Mortality and Need for Intensive Care Admission in COVID-19 Patients Evaluated in the Emergency Department. Ultrasound Med. Biol. 2020, 46, 2927–2937. [Google Scholar] [CrossRef] [PubMed]
  27. Lichter, Y.; Topilsky, Y.; Taieb, P.; Banai, A.; Hochstadt, A.; Merdler, I.; Gal Oz, A.; Vine, J.; Goren, O.; Cohen, B.; et al. Lung Ultrasound Predicts Clinical Course and Outcomes in COVID-19 Patients. Intensive Care Med. 2020, 46, 1873–1883. [Google Scholar] [CrossRef] [PubMed]
  28. Persona, P.; Valeri, I.; Zarantonello, F.; Forin, E.; Sella, N.; Andreatta, G.; Correale, C.; Serra, E.; Boscolo, A.; Volpicelli, G.; et al. Patients in Intensive Care Unit for COVID-19 Pneumonia: The Lung Ultrasound Patterns at Admission and Discharge. An Observational Pilot Study. Ultrasound J. 2021, 13, 10. [Google Scholar] [CrossRef] [PubMed]
  29. Bellani, G.; Grasselli, G.; Cecconi, M.; Antolini, L.; Borelli, M.; De Giacomi, F.; Bosio, G.; Latronico, N.; Filippini, M.; Gemma, M.; et al. Noninvasive Ventilatory Support of Patients with COVID-19 Outside the Intensive Care Units (WARd-COVID). Ann. ATS 2021, 18, 1020–1026. [Google Scholar] [CrossRef] [PubMed]
  30. Xiao, N.; Cooper, J.G.; Godbe, J.M.; Bechel, M.A.; Scott, M.B.; Nguyen, E.; McCarthy, D.M.; Abboud, S.; Allen, B.D.; Parekh, N.D. Chest Radiograph at Admission Predicts Early Intubation among Inpatient COVID-19 Patients. Eur. Radiol. 2021, 31, 2825–2832. [Google Scholar] [CrossRef] [PubMed]
  31. Fusco, R.; Grassi, R.; Granata, V.; Setola, S.V.; Grassi, F.; Cozzi, D.; Pecori, B.; Izzo, F.; Petrillo, A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-Ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. JPM 2021, 11, 993. [Google Scholar] [CrossRef] [PubMed]
  32. Grasselli, G.; Greco, M.; Zanella, A.; Albano, G.; Antonelli, M.; Bellani, G.; Bonanomi, E.; Cabrini, L.; Carlesso, E.; Castelli, G.; et al. Risk Factors Associated with Mortality Among Patients with COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Int. Med. 2020, 180, 1345. [Google Scholar] [CrossRef] [PubMed]
  33. World Health Organization. Use of Chest Imaging in COVID-19: A Rapid Advice Guide, 11 June 2020; World Health Organization: Geneva, Switzerland, 2020; Available online: https://apps.who.int/iris/handle/10665/332336 (accessed on 22 November 2021).
  34. Fortarezza, F.; Boscolo, A.; Pezzuto, F.; Lunardi, F.; Jesús Acosta, M.; Giraudo, C.; Del Vecchio, C.; Sella, N.; Tiberio, I.; Godi, I.; et al. Proven COVID-19-Associated Pulmonary Aspergillosis in Patients with Severe Respiratory Failure. Mycoses 2021, 64, 1223–1229. [Google Scholar] [CrossRef] [PubMed]
  35. Calabrese, F.; Pezzuto, F.; Fortarezza, F.; Boscolo, A.; Lunardi, F.; Giraudo, C.; Cattelan, A.; Del Vecchio, C.; Lorenzoni, G.; Vedovelli, L.; et al. Machine Learning-based Analysis of Alveolar and Vascular Injury in SARS-CoV-2 Acute Respiratory Failure. J. Pathol. 2021, 254, 173–184. [Google Scholar] [CrossRef] [PubMed]
  36. Cocconcelli, E.; Biondini, D.; Giraudo, C.; Lococo, S.; Bernardinello, N.; Fichera, G.; Barbiero, G.; Castelli, G.; Cavinato, S.; Ferrari, A.; et al. Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19. J Clin. Med. 2020, 9, 2990. [Google Scholar] [CrossRef] [PubMed]
  37. Brakohiapa, E.K.K.; Sarkodie, B.; Botwe, B.O.; Dzefi-Tettey, K.; Anim, D.A.; Edzie, E.K.; Goleku, P.N.; Jimah, B.B.; Amankwa, A.T. Comparing radiological presentations of first and second strains of COVID-19 infections in a low-resource country. Heliyon 2021, 7, e07818. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Patient selection flowchart. Abbreviations: NIRS, noninvasive respiratory support; ICU, intensive care unit; HFNO, high-flow nasal oxygen; CPAP, continuous positive airway pressure; NIV, noninvasive ventilation.
Figure 1. Patient selection flowchart. Abbreviations: NIRS, noninvasive respiratory support; ICU, intensive care unit; HFNO, high-flow nasal oxygen; CPAP, continuous positive airway pressure; NIV, noninvasive ventilation.
Jcm 11 01636 g001
Figure 2. Chest X-rays at noninvasive ventilation (NIV) initiation (a,b) and after 72 h (c,d) in two representative male patients (53 years old in (a,c) and 57 years old in (b,d)) affected by acute hypoxemic respiratory failure secondary to coronavirus disease-19. Although the two patients had similar CARE scores at NIV initiation and both showed an improvement in the score 72 h after the onset of noninvasive ventilation, only one patient received endotracheal intubation (after 2 days).
Figure 2. Chest X-rays at noninvasive ventilation (NIV) initiation (a,b) and after 72 h (c,d) in two representative male patients (53 years old in (a,c) and 57 years old in (b,d)) affected by acute hypoxemic respiratory failure secondary to coronavirus disease-19. Although the two patients had similar CARE scores at NIV initiation and both showed an improvement in the score 72 h after the onset of noninvasive ventilation, only one patient received endotracheal intubation (after 2 days).
Jcm 11 01636 g002
Table 1. Patients’ baseline characteristics.
Table 1. Patients’ baseline characteristics.
VariableAll Patients
(n = 142)
No Intubation
(n = 59)
Intubation
(n = 83)
p-Value
Age (years)69 (58–75)70 (60–79)69 (58–73)0.09
Weight (kg)78 (69–97)76 (68–96)79 (72–102)0.43
Body mass index (kg/m2)26 (22–31)25 (22–32)27 (24–30)0.66
Female gender (n [%])44 (31)19 (32)25 (30)0.86
Hypertension (n [%])81 (57)35 (59)46 (55)0.86
Obesity (n [%])45 (32)14 (24)31 (37)0.10
Diabetes (n [%])38 (27)19 (32)19 (23)0.26
Days since symptom onset6 (4–9)6 (3–8)7 (4–10)0.04
SOFA score3 (2–4)2 (2–3)3 (2–4)<0.01
Charlson comorbidity index3 (2–5)3 (2–5)3 (2–4)0.10
C-reactive protein (mg/L)97 (58–160)90 (41–123)113 (62–180)0.04
Procalcitonin (μg/L)0.18 (0.06–0.48)0.13 (0.06–0.48)0.19 (0.07–0.47)0.56
D-dimer (μg/L)323 (171–670)294 (150–523)335 (200–801)0.20
Leukocyte count (× 109 cells/L)7.58 (4.84–10.57)6.84 (3.32–9.60)7.81 (5.98–11.26)0.03
Lymphocyte count (× 109 cells/L)0.80 (0.55–1.11)0.78 (0.48–1.22)0.80 (0.59–1.10)0.75
IL-6 (pg/mL)55 (31–148)51 (26–99)67 (39–165)0.03
PaO2/FiO2 (mmHg)118 (90–160)148 (105–177)104 (78–134)<0.01
PaCO2 (mmHg)35 (31–38)35 (30–38)35 (31–38)0.75
Data are reported as the median (interquartile range) or number (percentage), as appropriate. Wilcoxon’s rank-sum test and Fisher’s exact test were applied, as appropriate. Abbreviations: SOFA, sequential organ failure assessment; IL-6, interleukin-6; PaO2/FiO2, arterial partial pressure of oxygen-to-inspired oxygen fraction ratio; PaCO2, arterial partial pressure of carbon dioxide.
Table 2. Patients’ outcomes.
Table 2. Patients’ outcomes.
VariableAll Patients (n = 142)No Intubation (n = 59)Intubation (n = 83)p-Value
Pronation (n [%])85 (60)14 (24)71 (86)<0.01
Duration of invasive mechanical ventilation (days)n.a.n.a.8 (6–13)n.a.
Hospital length of stay (days)22 (14–32)16 (12–22)29 (21–41)<0.01
Hospital mortality (n [%])20 (14)1 (2)19 (23)<0.01
Data are reported as the median (interquartile range) or number (percentage), as appropriate. Wilcoxon’s rank-sum test and Fisher’s exact test were applied, as appropriate. Abbreviations: n.a., not appropriate.
Table 3. The CARE score.
Table 3. The CARE score.
CARE ScoreAll Patients (n = 142)No Intubation (n = 59)Intubation (n = 83)p-Value
First CARE score9 (6–14)10 (6–13)9 (5–15)0.98
Second CARE score8 (4–14) *10 (5–17)8 (3–12) *0.04
Delta CARE score−1 (−5–3)−1 (−4–6)−2 (−6–2)0.01
Data are reported as the median (interquartile range) or number (percentage), as appropriate. Wilcoxon’s rank-sum test, Fisher’s exact test, and Wilcoxon’s signed-rank test were applied, as appropriate. The delta CARE score is the difference between the first and the second CARE score. * p < 0.05 from Wilcoxon’s signed-rank test assessing the change in the CARE score between the first and the second chest X-ray.
Table 4. Logistic regression for endotracheal intubation.
Table 4. Logistic regression for endotracheal intubation.
VariableUnivariableMultivariable
OR (95% CI)p-ValueOR (95% CI)p-Value
First CARE score1.01 (0.96–1.06)0.69
Age0.97 (0.94–1.00)0.07
Female gender0.91 (0.44–1.86)0.79
Days since symptom onset1.09 (1.00–1.20)0.06
SOFA score1.55 (1.15–2.10)<0.011.40 (0.99–1.99)0.06
Charlson comorbidity index0.86 (0.75–1.00)0.040.79 (0.65–0.95)0.01
C-reactive protein1.01 (1.00–1.01)0.041.01 (1.00–1.01)0.03
Procalcitonin1.06 (0.92–1.22)0.40
D-dimer1.00 (1.00–1.00)0.66
Leukocyte count1.06 (0.99–1.14)0.88
Lymphocyte count0.81 (0.60–1.09)0.17
IL-61.00 (1.00–1.01)0.11
PaO2/FiO20.99 (0.98–1.00)<0.010.99 (0.98–1.00)0.01
PaCO21.03 (0.98–1.09)0.22
Abbreviations: OR, odds ratio; CI, confidence interval; SOFA, sequential organ failure assessment; IL6, interleukin-6; PaO2/FiO2, arterial partial pressure of oxygen-to-inspired oxygen fraction ratio; PaCO2, arterial partial pressure of carbon dioxide. The variance inflation factors were 1.12 for the SOFA score, 1.13 for the Charlson comorbidity index, 1.02 for C-reactive protein, and 1.11 for PaO2/FiO2.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pettenuzzo, T.; Giraudo, C.; Fichera, G.; Della Paolera, M.; Tocco, M.; Weber, M.; Gorgi, D.; Carlucci, S.; Lionello, F.; Lococo, S.; et al. Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support. J. Clin. Med. 2022, 11, 1636. https://doi.org/10.3390/jcm11061636

AMA Style

Pettenuzzo T, Giraudo C, Fichera G, Della Paolera M, Tocco M, Weber M, Gorgi D, Carlucci S, Lionello F, Lococo S, et al. Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support. Journal of Clinical Medicine. 2022; 11(6):1636. https://doi.org/10.3390/jcm11061636

Chicago/Turabian Style

Pettenuzzo, Tommaso, Chiara Giraudo, Giulia Fichera, Michele Della Paolera, Martina Tocco, Michael Weber, Davide Gorgi, Silvia Carlucci, Federico Lionello, Sara Lococo, and et al. 2022. "Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support" Journal of Clinical Medicine 11, no. 6: 1636. https://doi.org/10.3390/jcm11061636

APA Style

Pettenuzzo, T., Giraudo, C., Fichera, G., Della Paolera, M., Tocco, M., Weber, M., Gorgi, D., Carlucci, S., Lionello, F., Lococo, S., Boscolo, A., De Cassai, A., Pasin, L., Rossato, M., Vianello, A., Vettor, R., Sella, N., & Navalesi, P. (2022). Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support. Journal of Clinical Medicine, 11(6), 1636. https://doi.org/10.3390/jcm11061636

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