Factors Associated With the Intubation of Patients With Acute Respiratory Failure and Their Impact on Mortality: a Retrospective Cohort Study

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Identifying the factors that lead to this need is crucial, but there are few studies on the evolution of these factors from the onset of symptoms to respiratory failure. This study aims to identify risk factors for invasive mechanical ventilation as well as clinical outcomes in patients with acute respiratory failure considering the time from the onset of symptoms to respiratory failure. Methods Retrospective cohort study with patients hospitalized between May 1, 2020 and May 1, 2021. Patients over 18 years of age admitted to Intermediate and Intensive Care Units with positive polymerase chain reaction for SARS-CoV-2, chest computed tomography and inflammatory markers performed within 72 hours of admission were included. Patients with chronic obstructive pulmonary disease using home oxygen, intubation not related to Covid-19, heart failure, previous tracheostomy and hospitalization of less than 24 hours were excluded. The main outcome was to identify the factors that determined tracheal intubation and the evolution of these patients. Results Of the 852 patients treated, 302 were excluded, leaving 550, of which 346 required intubation. Intubated patients had a higher body mass index (p = 0.02), a higher SAPS-3 (p < 0.001) and a shorter time from symptom onset to hospitalization (p < 0.001). Until the eighth day of hospitalization, these patients had higher levels of C-Reactive Protein (p < 0.001), Interleukin-6 (p = 0.003) and D-dimer (p < 0.001). Chest computed tomography scans revealed a larger area of ​​lung injury since admission. In the Cox model, SAPS-3 (HR = 1.028, 95%CI 1.002–1.055, p = 0.038) and time to intubation (HR = 1.118, 95%CI 1.021–1.224, p = 0.016) were independent risk factors for mortality. Patients intubated 15 days after the onset of symptoms had a higher risk of mortality (OR = 2.13, 95% CI 1.07–4.23). At intubation, the average respiratory rate was 27.5 breaths per minute, with 85% of FiO2 and ROX index of 4.37. The use of non-invasive ventilatory support was longer in the quartile with more than 15 days until intubation (median of 5 [ 3 – 7 ] days) and the use of a high-flow nasal cannula was associated with a longer time to decide to intubate (p = 0.002). Conclusion In patients with Covid-19 and acute respiratory failure, later intubation was associated with higher mortality. Non-invasive ventilatory support strategies can be used as long as there is no delay in using an invasive strategy when necessary. acute respiratory failure Covid-19 inflammatory markers intubation mechanical ventilation SARS-CoV-2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Acute respiratory failure can occur due to different pathologies, which may be due to direct lung injury, such as pneumonia, or diseases in which the primary injury does not occur in the lung parenchyma, such as pulmonary thromboembolism (1). Once respiratory failure occurs, patients are treated with supplemental oxygen using conventional devices, non-invasive ventilation, high-flow nasal cannula and, depending on the condition's evolution, invasive mechanical ventilation. (1, 2). It is known that Covid-19 presents, at least initially, a non-specific clinical course and that up to 17% of patients may require mechanical ventilation, whether invasive or non-invasive (3). Especially at the beginning of the condition, there is a disproportion in severity between the clinical findings, laboratory and radiological tests with patients deteriorating rapidly at a later stage of the disease (4). The patient is intubated in a decision-making process that may involve level of consciousness, respiratory rate, oxygen saturation, fraction of inspired oxygen and breathing pattern. However, there is no objective, dichotomous and prospectively validated intubation criteria for orotracheal intubation (OTI) in patients with hypoxemic respiratory failure (5). Therefore, there may be significant differences between doctors regarding the correct time to intubate a patient and this possible delay may impact the clinical outcome (6). Studies have also demonstrated the association between inflammatory markers and increased mortality in patients affected by pneumonia, whether viral or bacterial (7–9). Therefore, this study seeks to characterize the risk factors associated with tracheal intubation and mortality in patients with acute respiratory failure. Differing from other studies on the subject that characterize these data temporally from admission to the Intensive Care Unit (ICU), in the present study, in an innovative way, these outcomes as well as radiological changes and inflammatory markers are analyzed and cross-referenced from the onset of symptoms. Methods The present study is a retrospective cohort study and for this reason it does not have a clinical trial number. It is registered on Plataforma Brasil ( https://plataformabrasil.saude.gov.br ), which is a national and unified database of research records involving human beings linked to the brazilian federal government where it is approved by the Ethics Committee in institution research according to CAAE number 49630021.7.0000.0071. Likewise, this study also has approval from the Research Project Management System of the institution where it was carried out under number 4703-21. Due to its retrospective nature, the Ethics Committee waived the need for a Free and Informed Consent Form. Based on this, a cohort study was carried out that analyzed the association between initial care, tomographic findings and serum inflammatory markers from hospital admission to the 8th day of symptoms and the occurrence of respiratory failure as a result of SARS-CoV-2 infection. The research was conducted in a reference quaternary hospital in Brazil, including patients consecutively admitted to ICU and Intermediate Care Unit due to Covid-19 over the course of a year, from May 1, 2020 to May 1, 2021. Furthermore, the manuscript adhered to the guidelines proposed in the STROBE guidelines. Inclusion criteria Patients over 18 years of age admitted to ICU and Intermediate Care Unit due to Covid-19; positive viral Polymerase Chain Reaction (PCR) for SARS-CoV-2; hospital admission with up to 15 days of symptoms of SARS-CoV-2 infection; chest computed tomography (CT) and inflammatory markers performed within 72 hours of hospital. Exclusion Criteria Patients with chronic lung disease using home oxygen prior to Covid-19 contamination; need for tracheal intubation unrelated to Covid-19; heart failure with ejection fraction less than 40%; tracheostomy prior to hospital admission and hospital stay of less than 24 hours. Outcomes Primary Investigate risk factors associated with OTI and its impact on the mortality of patients with respiratory failure due to Covid-19 considering the time from the onset of symptoms and their temporal evolution until OTI. Secondary Compare the clinical characteristics between patients who did or did not require invasive mechanical ventilation, evaluate the temporal evolution of inflammatory markers (C-reactive protein, D-dimer, Ferritin and Interleukin-6) and correlate them with chest CT findings, evaluate the images of chest CT within 3 weeks of the onset of symptoms, correlating the previous use of non-invasive respiratory support with the time elapsed until tracheal intubation. Sample size calculation The sample size was calculated based on an estimate of the prevalence of respiratory failure due to Covid-19, considering an expected incidence rate of OTI. Estimating that 70% of patients with Covid-19 admitted to the ICU or Intermediate Care Unit require some respiratory support and of these, 30% require OTI (4), for a type I error of 5% and sample power of 95% (Reason negative sample OTI/positive OTI = 1/2), the total expected sample was at least 345 consecutively admitted patients and of these, 230 patients requiring invasive mechanical ventilation, however considering the retrospective nature of the study and the possibility of loss of 30% of data and patient follow-up, a minimum total of 450 patients needed to be included in the study. Collection variables The data analyzed were collected from a de-identified database and the variables collected were: Date of admission to the ICU or Intermediate Care Unit, age (years), sex (male or female), Simplified Acute Physiology Score 3 (10) (SAPS-3) on admission to the ICU, presence of comorbidities (Diabetes Mellitus, Systemic Arterial Hypertension, Chronic Obstructive Pulmonary Disease, Bronchial Asthma, Smoking, Neoplasia); need for ventilatory support (yes or no); mechanical ventilation time (days); date of hospital discharge or death; length of hospital stay (days); laboratory tests (C-reactive protein, D-dimer, Ferritin and Interleukin-6) measured daily until the eighth day and chest CT images in weeks 1, 2 and 3 of symptoms. In order to allow a more detailed analysis of the time between the onset of symptoms and OTI, we divided this variable into quartiles with similar population size. This allowed us to identify patterns or associations between different time intervals and outcomes, providing a more comprehensive understanding of the relationship between time to intubation and in-hospital mortality. Data reliability Patients consecutively admitted to the hospital's ICU and Intermediate Care Unit during a period of 1 year were retrospectively searched in order to complete the entire calculated sample and those who met the inclusion criteria were eligible for the study analysis. The tomographic extension as a percentage of ground-glass involvement as well as the presence or absence of consolidations were evaluated. Parallel to this analysis, the behavior trend of the previously listed inflammatory markers was verified. Patients were monitored until hospital discharge or death. Ethical aspects The study followed the ethical principles of research involving human beings of Resolution 196/96 of the National Health Council (BRAZIL, 1996) respecting the fundamental principles of autonomy, beneficence, non-maleficence, justice and equity. Ventilatory Support Protocol The hospital institution had a respiratory failure management protocol where non-invasive ventilation (HFNC or BiPAP) could be attempted for a period of up to 30 minutes aiming for a FiO2 ≤ 50% and in the case of BiPAP also a target of EPAP ≤ 10 cm H2O and a pressure delta ≤ 10 cm H2O aiming for a respiratory rate ≤ 24 bpm and SpO2 ≥ 94%. If this protocol failed, the patient underwent OTI (Fig. 1 ) (11). Intensive Care Unit and Intermediate Care Unit Admission Protocol Patients requiring oxygen supplementation via a nasal cannula with flow > 3 lpm or using NIV to maintain an SpO2 > 94% and/or respiratory rate ≤ 24 bpm were admitted to the Intermediate Care Unit. Patients using invasive mechanical ventilation, hemodynamic instability requiring vasopressors or lactate ≥ 36 mg/dl were admitted to the ICU. Chest computed tomography evaluation The tomographic findings were described according to the nomenclature defined by the Fleischner Society for thoracic radiological findings using the terms ground glass, mosaic paving and consolidations (12). The quantitative assessment of ground-glass opacities was carried out through an adaptation of the Chest CT Severity Score proposed by Tsakok et al. where lung involvement by ground-glass was quantified as ≤ 25%, > 25% and ≤ 50% or > 50% (13, 14). Image evaluations were performed according to institutional protocol and interpreted by a radiologist employed by the institution with a subspecialty in thoracic radiology. Data analysis Categorical variables were presented as absolute and relative frequencies. Quantitative variables were presented as mean and standard deviation or as median and interquartile interval when appropriate. The Kolmogorov-Smirnov test was used to evaluate the distribution pattern of continuous numerical variables. Proportions were compared using the Chi-square test or Fisher's exact test if the assumptions for using the Chi-square were violated. Quantitative variables were compared with the Mann-Whitney test or uneven distribution ANOVA when appropriate and multiple comparisons were performed. For multiple comparisons, Bonferroni correction was performed for P values. The association between explanatory variables and response was assessed using COX regression models that compared event rates in intubated patients over the length of hospital stay. The selected variables submitted to the multiple regression analyzes were those with statistical significance (p < 0.05) and those that were clinically relevant as previously defined, that is, those that have direct impact or significant influence on the clinical outcome or health condition of the patient. Variables with substantial collinearity were excluded. The results of the regression analysis were expressed as risk ratios over the length of hospital stay and respective 95% confidence intervals. All significance probabilities (p values) presented were two-tailed. The p values were considered statistically significant when lower than 0.05. The Statistical Package for Social Sciences software version 26.0 (SPSS Inc.®; Chicago, IL, USA) was used to perform the analyses. Results During the period, 852 patients were admitted to the ICU and Intermediate Care Unit, but 149 were excluded due to symptoms lasting longer than 15 days or lack of PCR for Covid-19; 134 were ineligible for the study, with 39 also excluded due to missing laboratory or tomographic tests or a negative Covid-19 test later (Fig. 2 ). Regarding demographic characteristics and conditions prior to OTI, a higher Body Mass Index (BMI) (P = 0.02), a higher SAPS-3 (P < 0.001) and a shorter time from symptoms to hospital admission (P < 0.001) were evidenced in patients who required invasive mechanical ventilation. All other variables were statistically similar (Table 1 ). Table 1 Baseline characteristics upon admission to the ICU Variable All participants (n = 550) Tracheal intubation (n = 346) No tracheal intubation (n = 204) P value Age, years , mean (SD) 63.2 (15.3) 63.9 (15.3) 61.8 (15.3) 0.12 Male , n (%) 378 (68.7%) 234 (67.6%) 144 (70.6%) 0.47 BMI (kg/m2) , mean (SD) 29.1 (5.3) 29.4 (5.3) 28.4 (5.1) 0.02 SAPS-3 , mean (SD) 47 (10.3) 49.1 (10.7) 43.4 (8.4) < 0.001 Comorbidity, n (%) Asthma 42 (7.6%) 23 (6.6%) 19 (9.3%) 0.25 COPD 53 (9.6%) 36 (10.4%) 17 (8.3%) 0.43 Smoke 31 (5.6%) 18 (5.2%) 13 (6.4%) 0.65 Hypertension 324 (58.9%) 212 (61.3%) 112 (54.9%) 0.14 Diabetes 119 (35.8%) 132 (38.2%) 65 (31.9%) 0.14 Chronic kidney disease of any stage 8 (1.5%) 5(1.4%) 3 (1.5%) 0.98 Solid Cancer 44 (8.0%) 27 (7.8%) 17 (8.3%) 0.82 Hematological Cancer 10 (1.8%) 8 (2.3%) 2 (1.0%) 0.26 Ejection fraction (echocardiogram)% , mean (SD) 63.7 (7.3) 63.8 (7.4) 63.4 (7.2) 0.55 Duration of symptoms before hospital admission, days , median (II) 7 (5.0–10) 7.0 (5.0–9.0) 8.0 (6.0–10.0) < 0.001 Use of corticosteroids , n (%) 539 (98.0%) 342 (98.8%) 197 (96,6%) 0.07 Received vaccine for COVID-19 , n (%) 8 (1.5%) 6 (1.7%) 2 (1.0%) 0.477 N = number of patients; SD = standard deviation; BMI = body mass index; COPD = chronic obstructive pulmonary disease, ICU = intensive care unit, II = interquartile interval Patients who progressed to invasive mechanical ventilation had a higher CRP, Interleukin-6 and D-dimer values when compared to other patients. On the other hand, Ferritin showed no difference in 8 days comparing the two groups of patients (Fig. 3 ). Analyzing the CT scans, there was a greater area of consolidation, as well as a higher percentage of lung injury above 25% in patients who were intubated and they became more evident after the second and third weeks (Fig. 4 ). Regarding inflammatory markers, it was shown that elevated CRP and serum D-dimer were associated with a greater presence of consolidation on chest CT (Fig. 5 ). Variables that were clinically relevant to the outcome and statistically significant among intubated patients were included in a COX regression model to identify independent risk factors for mortality prior to OTI. Only the SAPS-3 score and the time elapsed from the onset of symptoms to OTI emerged as significant factors for mortality in this period (Table 2 ). Table 2 Risk factors for mortality in a multivariate Cox model prior to tracheal intubation Variables HR 95% CI P SAPS-3 1.028 1.002–1.055 0.038 BMI (kg/m²) 1.037 0.985–1.092 0.165 Duration of symptoms before hospital admission, days 0.928 0.830–1.037 0.189 Duration of symptoms until intubation (days) 1.118 1.021–1224 0.016 Oxygen saturation, % 0.984 0.906–1.069 0.705 ROX Index 0.968 0.804–1.166 0.731 IL-6 (pg/ml) 1.004 0,997- 1,011 0.213 C-Reactive Protein (mg/L) 0.994 0,982- 1,006 0.349 D-dimer (ng/dL) 0.999 0.998–1.001 0.923 BMI = body mass index, IL-6 = Interleukin-6 When dividing the time interval between the onset of symptoms and tracheal intubation into quartiles and relating them to hospital mortality, we observed a higher mortality rate in patients intubated 15 days after the onset of symptoms (OR = 2.13; 95% CI 1.07–4.23 ), while times shorter than 11 days were associated with a lower risk of death. This result remains significant even after adjustments for the SAPS 3 score and body mass index (BMI) (Fig. 6 ). Examining the group of patients undergoing OTI, it is seen that the average respiratory rate at the time of intubation was 27.5 breaths per minute (bpm) with an average FiO2 requirement of 85% and a ROX index of 4.37. When dividing the intubation time into quartiles and comparing these variables, we observed that there were no significant differences between the quartiles in relation to clinical signs. However, it was seen that the time of using non-invasive support was significantly longer in the quartile with more than 15 days for intubation since the onset of symptoms, with a median of 5 days [interquartile interval of 3–7 days], as well such as the length of stay in the ICU and hospital until intubation (Table 3 ). Table 3 Clinical conditions at the time of tracheal intubation comparing the groups according to the time to intubation from the onset of symptoms Time to tracheal intubation from the onset of symptoms All ≤ 9 days 10–11 days 12–14 days ≥ 15 days P value* N 346 99 83 94 69 Respiratory rate, mean (SD) 27.56 (7.33) 28.9 (8.4) 26.7 (7.0) 28.1 (7.4) 26.2 (6.3) 0.341 Fraction of inspired oxygen (%), mean (SD) 85.00 (16.25) 82.7 (0.2) 82.6 (0.2) 87.9 (0.1) 85.4 (0.2) 0.336 ROX index, mean (SD) 4.37 (1.59) 4.35 (1.7) 4.73 (2.0) 4.02 (1.1) 4.50 (1.5) 0.191 Oxygen saturation (%), mean (SD) 91.69 (3.23) 91.5 (2.9) 92.1 (2.5) 91.5 (3.4) 91.8 (4.0) 0.789 Duration of symptoms until intubation (days); median [II] 11.00 [9.00, 14.00] 8 [ 7 – 9 ] 11 [ 10 – 11 ] 13 [ 12 – 13 ] 16 [ 15 – 18 ] < 0.001 Length of hospital stay until tracheal intubation (days); median [II] 4 [ 2 – 6 ] 2 [ 1 – 4 ] 4 [3-5.25] 5 [ 3 – 7 ] 8 [ 6 – 10 ] < 0.001 Length of ICU stay until tracheal intubation); median [II] 1 [0-2.5] 0.5 [0–1] 1 [0–2] 1 [0–3] 4 [ 1 – 5 ] < 0.001 Time of use of NIV/HFNC pre-tracheal intubation (days); median [II] 2 [ 1 – 4 ] 1.0 [ 1 – 2 ] 2 [ 1 – 3 ] 2 [ 1 – 4 ] 5 [ 3 – 7 ] < 0.001 N = number of patients; SD = standard deviation; II = interquartil interval, *ANOVA test Analyzing the devices used separately before OTI and after the onset of symptoms, we found that the time to intubation from the onset of symptoms was longer (P = 0.002) for patients who used a HFNC with a median of 12 days compared to the other respiratory supports used prior to OTI (Fig. 7 ). In the analysis of patients stratified by the time from the onset of symptoms to OTI, we observed significant differences in therapy and outcomes. Although oxygenation remained stable, there were a greater number of prone positions in patients who had a longer time until intubation. Furthermore, the use of NIV and HFNC was more frequent in patients with longer intervals until obtaining a definitive airway (42% and 56.5% respectively, in intervals ≥ 15 days) (Table 4 ). Table 4 Clinical results among intubated patients, separating the time of intubation from the onset of symptoms into quartiles All ≤ 9 days 10–11 days 12–14 days ≥ 15 days P value* N 346 99 83 94 69 Length of stay in the ICU (days), median [II] 15.00 [10.00, 25.75] 18 [10.75–28.75] 14 [8.25–24.75] 14 [10-24.75] 16 [11-26.5] 0.758 Length of hospital stay (days), median [II] 30.00 [19.00, 46.00] 29.0 [19–54] 28 [17–41] 27 [ 19 – 38 ] 35 [24.75–55.75] 0.081 Mechanical ventilation (days), median [II] 11.00 [7.00, 20.00] 12 [7-20.7] 10 [7-18.75] 10 [ 7 – 17 ] 11 [7-23.25] 0.387 PEEP post OTI, median [II] 12 [ 12 – 15 ] 13 [ 12 – 15 ] 12 [ 12 – 15 ] 12 [ 10 – 15 ] 13.5 12–15] 0.517 PaO2/FIO2, median [II] 157 [115-210.5] 174 [123–220] 158 [118–211] 166 [116–202] 143 [106–186] 0.102 Prone position, n (%) 81 (23.5%) 19 (19.2%) 16 (19.3%) 24 (25.5%) 22 (31.9%) 0.04 Use of NIV, n (%) 145 (42.0%) 48 (48,5%) 35 (42,2%) 33 (35,1%) 29 (42,0%) 0.045 Use of HFNC, n (%) 170 (49.3%) 39 (39.4%) 38 (45.8%) 54 (57.4%) 39 (56.5%) 0.043 Need for dialysis, n (%) 139 (40.4%) 43 (43.4%) 27 (32.9%) 39 (41.5%) 30 (43.5%) 0.458 ICU = intensive care unit, NIV = non-invasive ventilation, HFNC = high flow nasal cannula Discussion Patients who develop hypoxemic respiratory failure and require OTI have greater inflammatory activity prior to intubation and develop radiological worsening of the lung injury. Furthermore, risk factors for hospital mortality prior to respiratory failure were higher SAPS-3 values ​​and a longer time for tracheal intubation from the onset of symptoms. Through the analysis of the relationship since the onset of symptoms, temporal surveillance of inflammatory markers as well as radiological evolution was possible. Intubation performed 15 days after the onset of symptoms demonstrated worsening survival. The appropriate time for OTI is a central concern since in addition to hypoxemia, the patient outside of invasive mechanical ventilation may present a higher ventilatory effort, leading to high transpulmonary pressures, stress and strain, which may culminate in the development of Patient Self-Inflicted Lung Injury (P-SILI) and thus potentiate lesions in an already diseased lung (15–17). The prolonged time for OTI often correlates with the use of non-invasive ventilatory support, however its use is not without risks (18). Respiratory fatigue, pulmonary atelectasis and inflammatory activity (biotrauma) can be exacerbated, causing damage to distant organs in addition to the already sick respiratory system, worsening the patient's prognosis (5, 16–21). It is known that patients with exacerbated COPD managed with non-invasive ventilatory support undergo intubation in up to 15–20% of cases, a number that is already high, however in patients with hypoxemic respiratory failure these numbers jump to 40–60% of failure rate with the non-invasive method (22, 23). As an aggravating factor, these patients with hypoxemic respiratory failure who progress to OTI will have higher mortality (22, 24), which is one of the reasons for the extreme importance of their monitoring in relation to method failure, whether through tidal volume in patients using NIV or ROX Index in patients using HFNC (2, 25). At the beginning of the Covid-19 pandemic, the indication was to intubate immediately (26, 27). However, with greater understanding of the disease, it was seen that these patients tolerated a degree of hypoxemia with few clinical manifestations, causing these patients to undergo invasive mechanical ventilation later (28). Therefore, later guidelines recommended that patients, before being intubated, undergo therapy with non-invasive ventilatory support (26). Even with a physiological rationale for P-SILI in patients on NIV, a meta-analysis of more than 8,000 patients with Covid-19 suggested that the timing of intubation may not have an impact on mortality. However, given the great heterogeneity of the studies analyzed on the topic, this cannot be considered a certainty (29). The cutoff point for early and late intubation is divided between 24 and 48 hours depending on the source used. Studies that used 24 hours after ICU admission as a cutoff found no significant differences in mortality (19, 30). Studies that used 48 hours as a cutoff point reported contradictory results, with some observing a higher mortality rate in patients intubated after this period and others finding no significant differences (31). In another publication, both cutoff points were analyzed in a prospective paired analysis study in Spain, showing an increased risk of mortality in patients undergoing invasive mechanical ventilation both after 24 and 48 hours (32). A retrospective observational study including only 40 patients with Covid-19 found lower mortality in patients intubated before 50 hours after ICU admission, but it is clear that the small sample size does not allow definitive conclusions to be drawn about the best time for intubation (33). In the present study, unlike other existing studies, the temporal analysis takes place not from hospital or ICU admission, but rather with the onset of Covid-19 symptoms, providing a more detailed analysis of the patient whether in terms of their clinical, radiological or laboratories trends. Analyzing the numbers presented, the median from the onset of symptoms to hospitalization was 10 days and the median from the onset of symptoms to OTI was 11 days, the fact indicates that OTI occurred 24 hours after hospitalization, corroborating all studies which showed a positive influence from this early intervention. Assessing the time window for OTI from the onset of symptoms provides a more accurate perspective on the evolution of the disease from the appearance of the first signs of infection and can help with early identification of rapidly deteriorating patients. This makes it possible to implement interventions before the condition reaches more advanced stages. ICU arrival time can vary significantly between patients and some may be admitted late due to factors such as access to medical care, initial screening or individual characteristics of the disease. By starting the count from the symptoms, these variations are sought to be mitigated. Analysis from the onset of symptoms allows for a more in-depth understanding of how the initial immunological response may influence the need for OTI and other clinical outcomes. Regarding inflammatory markers, we found higher values ​​of CRP, IL-6 and D-dimer in patients undergoing OTI. The topic of hyperinflammatory and hypoinflammatory patterns of ARDS has been increasingly studied in recent years and it is known that patients with greater inflammatory activity throughout the disease require higher doses of vasoactive drugs, administration of fluids, PEEP values ​​as well as they have higher mortality (34–36). The importance of constant assessment of the patient in relation to their fluid needs is highlighted here, since at the same time that hyperinflammatory patients need to use higher PEEP values ​​in an attempt to correct their hypoxemia, these same individuals also receive a greater amount of fluids, which can in itself worsen the lung ventilation/perfusion relationship, leading to worsening hypoxemia. Furthermore, we found an association of patients with greater inflammatory activity and failure to non-invasive pulmonary ventilation strategies and the consequent need for OTI. In addition, we also showed that patients infected by SARS-CoV-2 and who presented a hyperinflammatory pattern had a greater presence of pulmonary consolidations on chest CT. These points are worth highlighting, since today there is much discussion about the ARDS phenotype, but little is known about what to do about it. Many studies today focus on whether a personalized approach to treating ARDS is valid, as occurs in oncology, as there are still doubts about its impact on the clinical outcome of these individuals (36, 37). Although this study has points that deserve to be highlighted, such as a representative sample in a highly specialized hospital, follow-up for 1 year, thus including different phases of the pandemic and avoiding possible biases related to variations in treatments, available resources and knowledge of the disease throughout the period, it also contains some limitations such as its observational character being only a generator of hypotheses without actually verifying the real causal condition (38). Furthermore, the collection was carried out in a single center, which does not allow the generalization of the study, the reason for intubation was not registered and there is a lack of data regarding the treatments carried out on patients during hospital admission that could impact mortality. However, the sample was homogeneous with robust statistical analysis, adjusted for several demographic and greater severity variables for these patients; in the same way, the consecutive collection of patients prevented the possibility of selection bias. Conclusions In patients with Covid-19, the decision to intubate late was associated with a deleterious effect. Our data suggest that clinicians consider a window of less than 12 days from symptom onset to perform intubation. Furthermore, the increased inflammatory response may be a potential warning to determine appropriate ventilatory care for these patients. In patients with Covid-19 receiving non-invasive ventilatory support, especially patients using a high-flow nasal cannula, intubation cannot be delayed. Based on the observational nature of the data, this information should be validated in future studies. Declarations Ethics/Ethical Approval The present study is a retrospective cohort study and for this reason it does not have a clinical trial number. It is registered on Plataforma Brasil (https://plataformabrasil.saude.gov.br), which is a national and unified database of research records involving human beings linked to the brazilian federal government where it is approved by the Ethics Committee in institution research according to CAAE number 49630021.7.0000.0071. Likewise, this study also has approval from the Research Project Management System of the institution where it was carried out under number 4703-21. Due to its retrospective nature, the Ethics Committee waived the need for a Free and Informed Consent Form. Furthermore, the study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. Funding No funding or sponsorship was received for this study or publication of this article. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors declare that they have no competing interests. Thanking Patient Participant The team responsible for the study would like to thank the patients who made the data analysis carried out here possible. References Rochwerg B, Granton D, Wang DX, Helviz Y, Einav S, Frat JP, et al. High flow nasal cannula compared with conventional oxygen therapy for acute hypoxemic respiratory failure: a systematic review and meta-analysis. Intensive Care Med. 2019;45(5):563-72. Munshi L, Mancebo J, Brochard LJ. Noninvasive Respiratory Support for Adults with Acute Respiratory Failure. N Engl J Med. 2022;387(18):1688-98. Ranzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baiao F, Hamacher S, et al. Characterisation of the first 250,000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med. 2021;9(4):407-18. Chen Z, Fan H, Cai J, Li Y, Wu B, Hou Y, et al. High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. Eur J Radiol. 2020;126:108972. Romanelli A, Toigo P, Scarpati G, Caccavale A, Lauro G, Baldassarre D, et al. Predictor factors for non-invasive mechanical ventilation failure in severe COVID-19 patients in the intensive care unit: a single-center retrospective study. J Anesth Analg Crit Care. 2022;2(1):10. Barbas CSV, Mazza BF. Is It Possible to Predict Respiratory Evolution in COVID-19 Patients? Respiration. 2022;101(7):621-3. Mooiweer E, Luijk B, Bonten MJ, Ekkelenkamp MB. C-Reactive protein levels but not CRP dynamics predict mortality in patients with pneumococcal pneumonia. J Infect. 2011;62(4):314-6. Viasus D, Del Rio-Pertuz G, Simonetti AF, Garcia-Vidal C, Acosta-Reyes J, Garavito A, et al. Biomarkers for predicting short-term mortality in community-acquired pneumonia: A systematic review and meta-analysis. J Infect. 2016;72(3):273-82. Ayanian S, Reyes J, Lynn L, Teufel K. The association between biomarkers and clinical outcomes in novel coronavirus pneumonia in a US cohort. Biomark Med. 2020;14(12):1091-7. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, et al. SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005;31(10):1345-55. Correa TD, Matos GFJ, Bravim BA, Cordioli RL, Garrido A, Assuncao MSC, et al. Intensive support recommendations for critically-ill patients with suspected or confirmed COVID-19 infection. Einstein (Sao Paulo). 2020;18:eAE5793. Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008;246(3):697-722. Yang R, Li X, Liu H, Zhen Y, Zhang X, Xiong Q, et al. Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19. Radiol Cardiothorac Imaging. 2020;2(2):e200047. Esper Treml R, Caldonazo T, Barlem Hohmann F, Lima da Rocha D, Filho PHA, Mori AL, et al. Association of chest computed tomography severity score at ICU admission and respiratory outcomes in critically ill COVID-19 patients. PLoS One. 2024;19(5):e0299390. Cruces P, Retamal J, Hurtado DE, Erranz B, Iturrieta P, Gonzalez C, et al. A physiological approach to understand the role of respiratory effort in the progression of lung injury in SARS-CoV-2 infection. Crit Care. 2020;24(1):494. Wendel Garcia PD, Aguirre-Bermeo H, Buehler PK, Alfaro-Farias M, Yuen B, David S, et al. Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort. Crit Care. 2021;25(1):175. Manrique S, Claverias L, Magret M, Masclans JR, Bodi M, Trefler S, et al. Timing of intubation and ICU mortality in COVID-19 patients: a retrospective analysis of 4198 critically ill patients during the first and second waves. BMC Anesthesiol. 2023;23(1):140. Li J, Scott JB, Fink JB, Reed B, Roca O, Dhand R. Optimizing high-flow nasal cannula flow settings in adult hypoxemic patients based on peak inspiratory flow during tidal breathing. Ann Intensive Care. 2021;11(1):164. Mellado-Artigas R, Ferrando C, Martino F, Delbove A, Ferreyro BL, Darreau C, et al. Early intubation and patient-centered outcomes in septic shock: a secondary analysis of a prospective multicenter study. Crit Care. 2022;26(1):163. Yamamoto R, Kaito D, Homma K, Endo A, Tagami T, Suzuki M, et al. Early intubation and decreased in-hospital mortality in patients with coronavirus disease 2019. Crit Care. 2022;26(1):124. Grotberg JC, Kraft BD. Timing of Intubation in COVID-19: When It Is Too Early and When It Is Too Late. Crit Care Explor. 2023;5(2):e0863. Bellani G, Laffey JG, Pham T, Madotto F, Fan E, Brochard L, et al. Noninvasive Ventilation of Patients with Acute Respiratory Distress Syndrome. Insights from the LUNG SAFE Study. Am J Respir Crit Care Med. 2017;195(1):67-77. Menga LS, Berardi C, Ruggiero E, Grieco DL, Antonelli M. Noninvasive respiratory support for acute respiratory failure due to COVID-19. Curr Opin Crit Care. 2022;28(1):25-50. Demoule A, Girou E, Richard JC, Taille S, Brochard L. Benefits and risks of success or failure of noninvasive ventilation. Intensive Care Med. 2006;32(11):1756-65. Papazian L, Aubron C, Brochard L, Chiche JD, Combes A, Dreyfuss D, et al. Formal guidelines: management of acute respiratory distress syndrome. Ann Intensive Care. 2019;9(1):69. Brewster DJ, Chrimes N, Do TB, Fraser K, Groombridge CJ, Higgs A, et al. Consensus statement: Safe Airway Society principles of airway management and tracheal intubation specific to the COVID-19 adult patient group. Med J Aust. 2020;212(10):472-81. Brown CA, 3rd, Mosier JM, Carlson JN, Gibbs MA. Pragmatic recommendations for intubating critically ill patients with suspected COVID-19. J Am Coll Emerg Physicians Open. 2020;1(2):80-4. Dhont S, Derom E, Van Braeckel E, Depuydt P, Lambrecht BN. The pathophysiology of 'happy' hypoxemia in COVID-19. Respir Res. 2020;21(1):198. Papoutsi E, Giannakoulis VG, Xourgia E, Routsi C, Kotanidou A, Siempos, II. Effect of timing of intubation on clinical outcomes of critically ill patients with COVID-19: a systematic review and meta-analysis of non-randomized cohort studies. Crit Care. 2021;25(1):121. Al-Tarbsheh A, Chong W, Oweis J, Saha B, Feustel P, Leamon A, et al. Clinical Outcomes of Early Versus Late Intubation in COVID-19 Patients. Cureus. 2022;14(1):e21669. Dupuis C, Bouadma L, de Montmollin E, Goldgran-Toledano D, Schwebel C, Reignier J, et al. Association Between Early Invasive Mechanical Ventilation and Day-60 Mortality in Acute Hypoxemic Respiratory Failure Related to Coronavirus Disease-2019 Pneumonia. Crit Care Explor. 2021;3(1):e0329. Riera J, Barbeta E, Tormos A, Mellado-Artigas R, Ceccato A, Motos A, et al. Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic: a matched analysis. Eur Respir J. 2023;61(3). Zhang Q, Shen J, Chen L, Li S, Zhang W, Jiang C, et al. Timing of invasive mechanic ventilation in critically ill patients with coronavirus disease 2019. J Trauma Acute Care Surg. 2020;89(6):1092-8. Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611-20. Famous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, et al. Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy. Am J Respir Crit Care Med. 2017;195(3):331-8. Matthay MA, Arabi YM, Siegel ER, Ware LB, Bos LDJ, Sinha P, et al. Phenotypes and personalized medicine in the acute respiratory distress syndrome. Intensive Care Med. 2020;46(12):2136-52. Pelosi P, Ball L, Barbas CSV, Bellomo R, Burns KEA, Einav S, et al. Personalized mechanical ventilation in acute respiratory distress syndrome. Crit Care. 2021;25(1):250. Caldonazo T, Treml RE, Vianna FSL, Tasoudis P, Kirov H, Mukharyamov M, et al. Outcomes comparison between the first and the subsequent SARS-CoV-2 waves - a systematic review and meta-analysis. Multidiscip Respir Med. 2023;18(1):933. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 01 Aug, 2024 Editor assigned by journal 31 Jul, 2024 Submission checks completed at journal 31 Jul, 2024 First submitted to journal 19 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4768432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334583631,"identity":"1dd3e16c-ac2e-4fb0-a1e3-9aab52b5cfb3","order_by":0,"name":"Fabio 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15:22:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4768432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4768432/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-025-03773-z","type":"published","date":"2025-07-03T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63913739,"identity":"9ee3e486-d0c1-4000-8b26-f0849ccfca4c","added_by":"auto","created_at":"2024-09-03 17:03:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36017,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision flowchart regarding ventilatory support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*The 30-minute NIV test is performed with a non-ventilated mask (without exhalation valve) and a double circuit on a mechanical ventilator with a barrier filter at the expiratory outlet.\u003c/p\u003e\n\u003cp\u003eAHRF: acute hypoxemic respiratory failure; OTI: orotracheal intubation; VNI: non-invasive ventilation; RR: respiratory rate; BPM: breaths per minute; SpO2: peripheral oxygen saturation; FiO2: inspiratory fraction of oxygen.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/6df1192d4c2af66dce812143.png"},{"id":63914450,"identity":"ec602ab2-8418-45ab-9c12-e9fe2a84cf9d","added_by":"auto","created_at":"2024-09-03 17:11:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":421661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/624e822a266697f7a1915324.png"},{"id":63913741,"identity":"3ccaece8-7314-4b13-9364-0f91449e2bb6","added_by":"auto","created_at":"2024-09-03 17:03:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":587618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInflammatory laboratory tests before intubation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA= Comparison of C-Reactive Protein between intubated and non-intubated patients; B= Comparison of Ferritin between intubated and non-intubated patients; C= Comparison of Interleukin-6 between intubated and non-intubated patients, D= Comparison of D-dimer between intubated and non-intubated patients. Dotted line represents intubated patients and solid line represents non-intubated patients.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/a03df3905cac6f552d239cc2.png"},{"id":63913745,"identity":"ee69b8c1-de0c-4399-87e0-7b265c7e0cf1","added_by":"auto","created_at":"2024-09-03 17:03:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":312146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComputed tomography follow-up per week\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA= percentage of consolidation in intubated and non-intubated patients; B= percentage of lung injury. Black bar represents intubated patients and light bar represents non-intubated patients.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/673293cda25a616de509b9c0.png"},{"id":63913744,"identity":"19f4b97c-0cdf-43a6-966a-05fbe0e417b8","added_by":"auto","created_at":"2024-09-03 17:03:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":329574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of inflammatory markers with the presence of consolidation on chest computed tomography per week\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/c3f2bc3fd29dd01affdcae93.png"},{"id":63913742,"identity":"89bb8056-b5d2-4c60-bb2b-fdf89f0aa4e5","added_by":"auto","created_at":"2024-09-03 17:03:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":466694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMortality in relation to time to intubation from the onset of symptoms. The top figure is unadjusted and the bottom figure is adjusted for SAPS 3 and BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Hospital mortality and days to indicate intubation; B: Odds ratio according to days to intubation adjusted for BMI and SAPS 3 using non-intubated patients as a reference; C: Odds ratio of only intubated patients according to days to intubation adjusted for BMI and SAPS 3 only with intubated patients.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/1a884e6fc491059c10570abe.png"},{"id":63914451,"identity":"e802f2a3-b5e8-4cd5-8f63-008a2290268c","added_by":"auto","created_at":"2024-09-03 17:11:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":170638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMedian time to intubation from the onset of symptoms in relation to respiratory support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bars represent the median time with respective 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/a8c40840b17e8f3984800f52.png"},{"id":86179038,"identity":"a9d21457-dedd-41fe-a1a1-fc4c5f0386a7","added_by":"auto","created_at":"2025-07-07 16:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4094656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4768432/v1/ac9e451b-fad9-4b8d-97f0-37d26db68a9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFactors Associated With the Intubation of Patients With Acute Respiratory Failure and Their Impact on Mortality: a Retrospective Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory failure can occur due to different pathologies, which may be due to direct lung injury, such as pneumonia, or diseases in which the primary injury does not occur in the lung parenchyma, such as pulmonary thromboembolism (1). Once respiratory failure occurs, patients are treated with supplemental oxygen using conventional devices, non-invasive ventilation, high-flow nasal cannula and, depending on the condition's evolution, invasive mechanical ventilation. (1, 2).\u003c/p\u003e \u003cp\u003eIt is known that Covid-19 presents, at least initially, a non-specific clinical course and that up to 17% of patients may require mechanical ventilation, whether invasive or non-invasive (3). Especially at the beginning of the condition, there is a disproportion in severity between the clinical findings, laboratory and radiological tests with patients deteriorating rapidly at a later stage of the disease (4). The patient is intubated in a decision-making process that may involve level of consciousness, respiratory rate, oxygen saturation, fraction of inspired oxygen and breathing pattern. However, there is no objective, dichotomous and prospectively validated intubation criteria for orotracheal intubation (OTI) in patients with hypoxemic respiratory failure (5). Therefore, there may be significant differences between doctors regarding the correct time to intubate a patient and this possible delay may impact the clinical outcome (6).\u003c/p\u003e \u003cp\u003eStudies have also demonstrated the association between inflammatory markers and increased mortality in patients affected by pneumonia, whether viral or bacterial (7\u0026ndash;9).\u003c/p\u003e \u003cp\u003eTherefore, this study seeks to characterize the risk factors associated with tracheal intubation and mortality in patients with acute respiratory failure. Differing from other studies on the subject that characterize these data temporally from admission to the Intensive Care Unit (ICU), in the present study, in an innovative way, these outcomes as well as radiological changes and inflammatory markers are analyzed and cross-referenced from the onset of symptoms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present study is a retrospective cohort study and for this reason it does not have a clinical trial number. It is registered on Plataforma Brasil (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plataformabrasil.saude.gov.br\u003c/span\u003e\u003c/span\u003e), which is a national and unified database of research records involving human beings linked to the brazilian federal government where it is approved by the Ethics Committee in institution research according to CAAE number 49630021.7.0000.0071. Likewise, this study also has approval from the Research Project Management System of the institution where it was carried out under number 4703-21. Due to its retrospective nature, the Ethics Committee waived the need for a Free and Informed Consent Form.\u003c/p\u003e\n\u003cp\u003eBased on this, a cohort study was carried out that analyzed the association between initial care, tomographic findings and serum inflammatory markers from hospital admission to the 8th day of symptoms and the occurrence of respiratory failure as a result of SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eThe research was conducted in a reference quaternary hospital in Brazil, including patients consecutively admitted to ICU and Intermediate Care Unit due to Covid-19 over the course of a year, from May 1, 2020 to May 1, 2021.\u003c/p\u003e\n\u003cp\u003eFurthermore, the manuscript adhered to the guidelines proposed in the STROBE guidelines.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eInclusion criteria\u003c/h2\u003e\n \u003cp\u003ePatients over 18 years of age admitted to ICU and Intermediate Care Unit due to Covid-19; positive viral Polymerase Chain Reaction (PCR) for SARS-CoV-2; hospital admission with up to 15 days of symptoms of SARS-CoV-2 infection; chest computed tomography (CT) and inflammatory markers performed within 72 hours of hospital.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eExclusion Criteria\u003c/h2\u003e\n \u003cp\u003ePatients with chronic lung disease using home oxygen prior to Covid-19 contamination; need for tracheal intubation unrelated to Covid-19; heart failure with ejection fraction less than 40%; tracheostomy prior to hospital admission and hospital stay of less than 24 hours.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eOutcomes\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003ePrimary\u003c/h2\u003e\n \u003cp\u003eInvestigate risk factors associated with OTI and its impact on the mortality of patients with respiratory failure due to Covid-19 considering the time from the onset of symptoms and their temporal evolution until OTI.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSecondary\u003c/h2\u003e\n \u003cp\u003eCompare the clinical characteristics between patients who did or did not require invasive mechanical ventilation, evaluate the temporal evolution of inflammatory markers (C-reactive protein, D-dimer, Ferritin and Interleukin-6) and correlate them with chest CT findings, evaluate the images of chest CT within 3 weeks of the onset of symptoms, correlating the previous use of non-invasive respiratory support with the time elapsed until tracheal intubation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSample size calculation\u003c/h2\u003e\n \u003cp\u003eThe sample size was calculated based on an estimate of the prevalence of respiratory failure due to Covid-19, considering an expected incidence rate of OTI.\u003c/p\u003e\n \u003cp\u003eEstimating that 70% of patients with Covid-19 admitted to the ICU or Intermediate Care Unit require some respiratory support and of these, 30% require OTI (4), for a type I error of 5% and sample power of 95% (Reason negative sample OTI/positive OTI\u0026thinsp;=\u0026thinsp;1/2), the total expected sample was at least 345 consecutively admitted patients and of these, 230 patients requiring invasive mechanical ventilation, however considering the retrospective nature of the study and the possibility of loss of 30% of data and patient follow-up, a minimum total of 450 patients needed to be included in the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCollection variables\u003c/h2\u003e\n \u003cp\u003eThe data analyzed were collected from a de-identified database and the variables collected were:\u003c/p\u003e\n \u003cp\u003eDate of admission to the ICU or Intermediate Care Unit, age (years), sex (male or female), Simplified Acute Physiology Score 3 (10) (SAPS-3) on admission to the ICU, presence of comorbidities (Diabetes Mellitus, Systemic Arterial Hypertension, Chronic Obstructive Pulmonary Disease, Bronchial Asthma, Smoking, Neoplasia); need for ventilatory support (yes or no); mechanical ventilation time (days); date of hospital discharge or death; length of hospital stay (days); laboratory tests (C-reactive protein, D-dimer, Ferritin and Interleukin-6) measured daily until the eighth day and chest CT images in weeks 1, 2 and 3 of symptoms.\u003c/p\u003e\n \u003cp\u003eIn order to allow a more detailed analysis of the time between the onset of symptoms and OTI, we divided this variable into quartiles with similar population size. This allowed us to identify patterns or associations between different time intervals and outcomes, providing a more comprehensive understanding of the relationship between time to intubation and in-hospital mortality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eData reliability\u003c/h2\u003e\n \u003cp\u003ePatients consecutively admitted to the hospital\u0026apos;s ICU and Intermediate Care Unit during a period of 1 year were retrospectively searched in order to complete the entire calculated sample and those who met the inclusion criteria were eligible for the study analysis. The tomographic extension as a percentage of ground-glass involvement as well as the presence or absence of consolidations were evaluated. Parallel to this analysis, the behavior trend of the previously listed inflammatory markers was verified. Patients were monitored until hospital discharge or death.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical aspects\u003c/h2\u003e\n \u003cp\u003eThe study followed the ethical principles of research involving human beings of Resolution 196/96 of the National Health Council (BRAZIL, 1996) respecting the fundamental principles of autonomy, beneficence, non-maleficence, justice and equity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eVentilatory Support Protocol\u003c/h2\u003e\n \u003cp\u003eThe hospital institution had a respiratory failure management protocol where non-invasive ventilation (HFNC or BiPAP) could be attempted for a period of up to 30 minutes aiming for a FiO2\u0026thinsp;\u0026le;\u0026thinsp;50% and in the case of BiPAP also a target of EPAP\u0026thinsp;\u0026le;\u0026thinsp;10 cm H2O and a pressure delta\u0026thinsp;\u0026le;\u0026thinsp;10 cm H2O aiming for a respiratory rate\u0026thinsp;\u0026le;\u0026thinsp;24 bpm and SpO2\u0026thinsp;\u0026ge;\u0026thinsp;94%. If this protocol failed, the patient underwent OTI (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (11).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eIntensive Care Unit and Intermediate Care Unit Admission Protocol\u003c/h2\u003e\n \u003cp\u003ePatients requiring oxygen supplementation via a nasal cannula with flow\u0026thinsp;\u0026gt;\u0026thinsp;3 lpm or using NIV to maintain an SpO2\u0026thinsp;\u0026gt;\u0026thinsp;94% and/or respiratory rate\u0026thinsp;\u0026le;\u0026thinsp;24 bpm were admitted to the Intermediate Care Unit. Patients using invasive mechanical ventilation, hemodynamic instability requiring vasopressors or lactate\u0026thinsp;\u0026ge;\u0026thinsp;36 mg/dl were admitted to the ICU.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eChest computed tomography evaluation\u003c/h2\u003e\n \u003cp\u003eThe tomographic findings were described according to the nomenclature defined by the Fleischner Society for thoracic radiological findings using the terms ground glass, mosaic paving and consolidations (12). The quantitative assessment of ground-glass opacities was carried out through an adaptation of the Chest CT Severity Score proposed by Tsakok et al. where lung involvement by ground-glass was quantified as \u0026le;\u0026thinsp;25%, \u0026gt;\u0026thinsp;25% and \u0026le;\u0026thinsp;50% or \u0026gt;\u0026thinsp;50% (13, 14).\u003c/p\u003e\n \u003cp\u003eImage evaluations were performed according to institutional protocol and interpreted by a radiologist employed by the institution with a subspecialty in thoracic radiology.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eCategorical variables were presented as absolute and relative frequencies. Quantitative variables were presented as mean and standard deviation or as median and interquartile interval when appropriate. The Kolmogorov-Smirnov test was used to evaluate the distribution pattern of continuous numerical variables.\u003c/p\u003e\n \u003cp\u003eProportions were compared using the Chi-square test or Fisher\u0026apos;s exact test if the assumptions for using the Chi-square were violated. Quantitative variables were compared with the Mann-Whitney test or uneven distribution ANOVA when appropriate and multiple comparisons were performed. For multiple comparisons, Bonferroni correction was performed for P values.\u003c/p\u003e\n \u003cp\u003eThe association between explanatory variables and response was assessed using COX regression models that compared event rates in intubated patients over the length of hospital stay. The selected variables submitted to the multiple regression analyzes were those with statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and those that were clinically relevant as previously defined, that is, those that have direct impact or significant influence on the clinical outcome or health condition of the patient. Variables with substantial collinearity were excluded. The results of the regression analysis were expressed as risk ratios over the length of hospital stay and respective 95% confidence intervals.\u003c/p\u003e\n \u003cp\u003eAll significance probabilities (p values) presented were two-tailed. The p values were considered statistically significant when lower than 0.05. The Statistical Package for Social Sciences software version 26.0 (SPSS Inc.\u0026reg;; Chicago, IL, USA) was used to perform the analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the period, 852 patients were admitted to the ICU and Intermediate Care Unit, but 149 were excluded due to symptoms lasting longer than 15 days or lack of PCR for Covid-19; 134 were ineligible for the study, with 39 also excluded due to missing laboratory or tomographic tests or a negative Covid-19 test later (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding demographic characteristics and conditions prior to OTI, a higher Body Mass Index (BMI) (P\u0026thinsp;=\u0026thinsp;0.02), a higher SAPS-3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a shorter time from symptoms to hospital admission (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were evidenced in patients who required invasive mechanical ventilation. All other variables were statistically similar (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics upon admission to the ICU\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll participants (n\u0026thinsp;=\u0026thinsp;550)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTracheal intubation (n\u0026thinsp;=\u0026thinsp;346)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo tracheal intubation (n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.2 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.9 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.8 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAPS-3\u003c/strong\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.4 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic kidney disease of any stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5(1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolid Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematological Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEjection fraction (echocardiogram)%\u003c/strong\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.7 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.8 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.4 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of symptoms before hospital admission, days\u003c/strong\u003e, median (II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (5.0\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0 (6.0\u0026ndash;10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of corticosteroids\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e539 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342 (98.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (96,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceived vaccine for COVID-19\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eN\u0026thinsp;=\u0026thinsp;number of patients; SD\u0026thinsp;=\u0026thinsp;standard deviation; BMI\u0026thinsp;=\u0026thinsp;body mass index; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease, ICU\u0026thinsp;=\u0026thinsp;intensive care unit, II\u0026thinsp;=\u0026thinsp;interquartile interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ePatients who progressed to invasive mechanical ventilation had a higher CRP, Interleukin-6 and D-dimer values when compared to other patients. On the other hand, Ferritin showed no difference in 8 days comparing the two groups of patients (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAnalyzing the CT scans, there was a greater area of consolidation, as well as a higher percentage of lung injury above 25% in patients who were intubated and they became more evident after the second and third weeks (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding inflammatory markers, it was shown that elevated CRP and serum D-dimer were associated with a greater presence of consolidation on chest CT (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eVariables that were clinically relevant to the outcome and statistically significant among intubated patients were included in a COX regression model to identify independent risk factors for mortality prior to OTI. Only the SAPS-3 score and the time elapsed from the onset of symptoms to OTI emerged as significant factors for mortality in this period (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk factors for mortality in a multivariate Cox model prior to tracheal intubation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAPS-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.002\u0026ndash;1.055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u0026ndash;1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of symptoms before hospital admission, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u0026ndash;1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of symptoms until intubation (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.021\u0026ndash;1224\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxygen saturation, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.906\u0026ndash;1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROX Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u0026ndash;1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,997- 1,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-Reactive Protein (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,982- 1,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD-dimer (ng/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u0026ndash;1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;body mass index, IL-6\u0026thinsp;=\u0026thinsp;Interleukin-6\u003c/p\u003e\n\u003cp\u003eWhen dividing the time interval between the onset of symptoms and tracheal intubation into quartiles and relating them to hospital mortality, we observed a higher mortality rate in patients intubated 15 days after the onset of symptoms (OR\u0026thinsp;=\u0026thinsp;2.13; 95% CI 1.07\u0026ndash;4.23 ), while times shorter than 11 days were associated with a lower risk of death. This result remains significant even after adjustments for the SAPS 3 score and body mass index (BMI) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eExamining the group of patients undergoing OTI, it is seen that the average respiratory rate at the time of intubation was 27.5 breaths per minute (bpm) with an average FiO2 requirement of 85% and a ROX index of 4.37. When dividing the intubation time into quartiles and comparing these variables, we observed that there were no significant differences between the quartiles in relation to clinical signs. However, it was seen that the time of using non-invasive support was significantly longer in the quartile with more than 15 days for intubation since the onset of symptoms, with a median of 5 days [interquartile interval of 3\u0026ndash;7 days], as well such as the length of stay in the ICU and hospital until intubation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical conditions at the time of tracheal intubation comparing the groups according to the time to intubation from the onset of symptoms\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime to tracheal intubation from the onset of symptoms\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;9 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;11 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u0026ndash;14 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;15 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory rate, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.56 (7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.2 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFraction of inspired oxygen (%), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.00 (16.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.7 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.6 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.9 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.4 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROX index, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.73 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.02 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxygen saturation (%), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.69 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.5 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.5 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.8 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of symptoms until intubation (days); median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.00 [9.00, 14.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of hospital stay until tracheal intubation (days); median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 [3-5.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of ICU stay until tracheal intubation); median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 [0-2.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 [0\u0026ndash;1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 [0\u0026ndash;2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 [0\u0026ndash;3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime of use of NIV/HFNC pre-tracheal intubation (days); median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;number of patients; SD\u0026thinsp;=\u0026thinsp;standard deviation; II\u0026thinsp;=\u0026thinsp;interquartil interval, *ANOVA test\u003c/p\u003e\n\u003cp\u003eAnalyzing the devices used separately before OTI and after the onset of symptoms, we found that the time to intubation from the onset of symptoms was longer (P\u0026thinsp;=\u0026thinsp;0.002) for patients who used a HFNC with a median of 12 days compared to the other respiratory supports used prior to OTI (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the analysis of patients stratified by the time from the onset of symptoms to OTI, we observed significant differences in therapy and outcomes. Although oxygenation remained stable, there were a greater number of prone positions in patients who had a longer time until intubation. Furthermore, the use of NIV and HFNC was more frequent in patients with longer intervals until obtaining a definitive airway (42% and 56.5% respectively, in intervals\u0026thinsp;\u0026ge;\u0026thinsp;15 days) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical results among intubated patients, separating the time of intubation from the onset of symptoms into quartiles\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;9 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;11 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u0026ndash;14 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;15 days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of stay in the ICU (days), median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.00 [10.00, 25.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 [10.75\u0026ndash;28.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 [8.25\u0026ndash;24.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 [10-24.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 [11-26.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of hospital stay (days), median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.00 [19.00, 46.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.0 [19\u0026ndash;54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 [17\u0026ndash;41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 [24.75\u0026ndash;55.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation (days), median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.00 [7.00, 20.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 [7-20.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 [7-18.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 [7-23.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEEP post OTI, median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 12\u0026ndash;15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaO2/FIO2, median [II]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 [115-210.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174 [123\u0026ndash;220]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 [118\u0026ndash;211]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 [116\u0026ndash;202]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 [106\u0026ndash;186]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProne position, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of NIV, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (48,5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (42,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (35,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (42,0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of HFNC, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeed for dialysis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (43.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eICU\u0026thinsp;=\u0026thinsp;intensive care unit, NIV\u0026thinsp;=\u0026thinsp;non-invasive ventilation, HFNC\u0026thinsp;=\u0026thinsp;high flow nasal cannula\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients who develop hypoxemic respiratory failure and require OTI have greater inflammatory activity prior to intubation and develop radiological worsening of the lung injury. Furthermore, risk factors for hospital mortality prior to respiratory failure were higher SAPS-3 values ​​and a longer time for tracheal intubation from the onset of symptoms.\u003c/p\u003e \u003cp\u003eThrough the analysis of the relationship since the onset of symptoms, temporal surveillance of inflammatory markers as well as radiological evolution was possible. Intubation performed 15 days after the onset of symptoms demonstrated worsening survival.\u003c/p\u003e \u003cp\u003eThe appropriate time for OTI is a central concern since in addition to hypoxemia, the patient outside of invasive mechanical ventilation may present a higher ventilatory effort, leading to high transpulmonary pressures, stress and strain, which may culminate in the development of Patient Self-Inflicted Lung Injury (P-SILI) and thus potentiate lesions in an already diseased lung (15\u0026ndash;17).\u003c/p\u003e \u003cp\u003eThe prolonged time for OTI often correlates with the use of non-invasive ventilatory support, however its use is not without risks (18). Respiratory fatigue, pulmonary atelectasis and inflammatory activity (biotrauma) can be exacerbated, causing damage to distant organs in addition to the already sick respiratory system, worsening the patient's prognosis (5, 16\u0026ndash;21). It is known that patients with exacerbated COPD managed with non-invasive ventilatory support undergo intubation in up to 15\u0026ndash;20% of cases, a number that is already high, however in patients with hypoxemic respiratory failure these numbers jump to 40\u0026ndash;60% of failure rate with the non-invasive method (22, 23). As an aggravating factor, these patients with hypoxemic respiratory failure who progress to OTI will have higher mortality (22, 24), which is one of the reasons for the extreme importance of their monitoring in relation to method failure, whether through tidal volume in patients using NIV or ROX Index in patients using HFNC (2, 25). At the beginning of the Covid-19 pandemic, the indication was to intubate immediately (26, 27). However, with greater understanding of the disease, it was seen that these patients tolerated a degree of hypoxemia with few clinical manifestations, causing these patients to undergo invasive mechanical ventilation later (28). Therefore, later guidelines recommended that patients, before being intubated, undergo therapy with non-invasive ventilatory support (26).\u003c/p\u003e \u003cp\u003eEven with a physiological rationale for P-SILI in patients on NIV, a meta-analysis of more than 8,000 patients with Covid-19 suggested that the timing of intubation may not have an impact on mortality. However, given the great heterogeneity of the studies analyzed on the topic, this cannot be considered a certainty (29).\u003c/p\u003e \u003cp\u003eThe cutoff point for early and late intubation is divided between 24 and 48 hours depending on the source used. Studies that used 24 hours after ICU admission as a cutoff found no significant differences in mortality (19, 30). Studies that used 48 hours as a cutoff point reported contradictory results, with some observing a higher mortality rate in patients intubated after this period and others finding no significant differences (31). In another publication, both cutoff points were analyzed in a prospective paired analysis study in Spain, showing an increased risk of mortality in patients undergoing invasive mechanical ventilation both after 24 and 48 hours (32). A retrospective observational study including only 40 patients with Covid-19 found lower mortality in patients intubated before 50 hours after ICU admission, but it is clear that the small sample size does not allow definitive conclusions to be drawn about the best time for intubation (33).\u003c/p\u003e \u003cp\u003eIn the present study, unlike other existing studies, the temporal analysis takes place not from hospital or ICU admission, but rather with the onset of Covid-19 symptoms, providing a more detailed analysis of the patient whether in terms of their clinical, radiological or laboratories trends. Analyzing the numbers presented, the median from the onset of symptoms to hospitalization was 10 days and the median from the onset of symptoms to OTI was 11 days, the fact indicates that OTI occurred 24 hours after hospitalization, corroborating all studies which showed a positive influence from this early intervention. Assessing the time window for OTI from the onset of symptoms provides a more accurate perspective on the evolution of the disease from the appearance of the first signs of infection and can help with early identification of rapidly deteriorating patients. This makes it possible to implement interventions before the condition reaches more advanced stages.\u003c/p\u003e \u003cp\u003eICU arrival time can vary significantly between patients and some may be admitted late due to factors such as access to medical care, initial screening or individual characteristics of the disease. By starting the count from the symptoms, these variations are sought to be mitigated. Analysis from the onset of symptoms allows for a more in-depth understanding of how the initial immunological response may influence the need for OTI and other clinical outcomes.\u003c/p\u003e \u003cp\u003eRegarding inflammatory markers, we found higher values ​​of CRP, IL-6 and D-dimer in patients undergoing OTI. The topic of hyperinflammatory and hypoinflammatory patterns of ARDS has been increasingly studied in recent years and it is known that patients with greater inflammatory activity throughout the disease require higher doses of vasoactive drugs, administration of fluids, PEEP values ​​as well as they have higher mortality (34\u0026ndash;36). The importance of constant assessment of the patient in relation to their fluid needs is highlighted here, since at the same time that hyperinflammatory patients need to use higher PEEP values ​​in an attempt to correct their hypoxemia, these same individuals also receive a greater amount of fluids, which can in itself worsen the lung ventilation/perfusion relationship, leading to worsening hypoxemia.\u003c/p\u003e \u003cp\u003eFurthermore, we found an association of patients with greater inflammatory activity and failure to non-invasive pulmonary ventilation strategies and the consequent need for OTI. In addition, we also showed that patients infected by SARS-CoV-2 and who presented a hyperinflammatory pattern had a greater presence of pulmonary consolidations on chest CT. These points are worth highlighting, since today there is much discussion about the ARDS phenotype, but little is known about what to do about it. Many studies today focus on whether a personalized approach to treating ARDS is valid, as occurs in oncology, as there are still doubts about its impact on the clinical outcome of these individuals (36, 37).\u003c/p\u003e \u003cp\u003eAlthough this study has points that deserve to be highlighted, such as a representative sample in a highly specialized hospital, follow-up for 1 year, thus including different phases of the pandemic and avoiding possible biases related to variations in treatments, available resources and knowledge of the disease throughout the period, it also contains some limitations such as its observational character being only a generator of hypotheses without actually verifying the real causal condition (38). Furthermore, the collection was carried out in a single center, which does not allow the generalization of the study, the reason for intubation was not registered and there is a lack of data regarding the treatments carried out on patients during hospital admission that could impact mortality. However, the sample was homogeneous with robust statistical analysis, adjusted for several demographic and greater severity variables for these patients; in the same way, the consecutive collection of patients prevented the possibility of selection bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn patients with Covid-19, the decision to intubate late was associated with a deleterious effect. Our data suggest that clinicians consider a window of less than 12 days from symptom onset to perform intubation. Furthermore, the increased inflammatory response may be a potential warning to determine appropriate ventilatory care for these patients. In patients with Covid-19 receiving non-invasive ventilatory support, especially patients using a high-flow nasal cannula, intubation cannot be delayed. Based on the observational nature of the data, this information should be validated in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics/Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study is a retrospective cohort study and for this reason it does not have a clinical trial number. It is registered on Plataforma Brasil (https://plataformabrasil.saude.gov.br), which is a national and unified database of research records involving human beings linked to the brazilian federal government where it is approved by the Ethics Committee in institution research according to CAAE number 49630021.7.0000.0071. Likewise, this study also has approval from the Research Project Management System of the institution where it was carried out under number 4703-21. Due to its retrospective nature, the Ethics Committee waived the need for a Free and Informed Consent Form.\u0026nbsp;Furthermore, the study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;No funding or sponsorship was received for this study or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available\u0026nbsp;from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThanking Patient Participant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe team responsible for the study would like to thank the patients who made the data analysis carried out here possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRochwerg B, Granton D, Wang DX, Helviz Y, Einav S, Frat JP, et al. High flow nasal cannula compared with conventional oxygen therapy for acute hypoxemic respiratory failure: a systematic review and meta-analysis. Intensive Care Med. 2019;45(5):563-72.\u003c/li\u003e\n \u003cli\u003eMunshi L, Mancebo J, Brochard LJ. Noninvasive Respiratory Support for Adults with Acute Respiratory Failure. N Engl J Med. 2022;387(18):1688-98.\u003c/li\u003e\n \u003cli\u003eRanzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baiao F, Hamacher S, et al. Characterisation of the first 250,000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med. 2021;9(4):407-18.\u003c/li\u003e\n \u003cli\u003eChen Z, Fan H, Cai J, Li Y, Wu B, Hou Y, et al. High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. Eur J Radiol. 2020;126:108972.\u003c/li\u003e\n \u003cli\u003eRomanelli A, Toigo P, Scarpati G, Caccavale A, Lauro G, Baldassarre D, et al. 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Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic: a matched analysis. Eur Respir J. 2023;61(3).\u003c/li\u003e\n \u003cli\u003eZhang Q, Shen J, Chen L, Li S, Zhang W, Jiang C, et al. Timing of invasive mechanic ventilation in critically ill patients with coronavirus disease 2019. J Trauma Acute Care Surg. 2020;89(6):1092-8.\u003c/li\u003e\n \u003cli\u003eCalfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611-20.\u003c/li\u003e\n \u003cli\u003eFamous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, et al. Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy. Am J Respir Crit Care Med. 2017;195(3):331-8.\u003c/li\u003e\n \u003cli\u003eMatthay MA, Arabi YM, Siegel ER, Ware LB, Bos LDJ, Sinha P, et al. Phenotypes and personalized medicine in the acute respiratory distress syndrome. Intensive Care Med. 2020;46(12):2136-52.\u003c/li\u003e\n \u003cli\u003ePelosi P, Ball L, Barbas CSV, Bellomo R, Burns KEA, Einav S, et al. Personalized mechanical ventilation in acute respiratory distress syndrome. Crit Care. 2021;25(1):250.\u003c/li\u003e\n \u003cli\u003eCaldonazo T, Treml RE, Vianna FSL, Tasoudis P, Kirov H, Mukharyamov M, et al. Outcomes comparison between the first and the subsequent SARS-CoV-2 waves - a systematic review and meta-analysis. Multidiscip Respir Med. 2023;18(1):933.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute respiratory failure, Covid-19, inflammatory markers, intubation, mechanical ventilation, SARS-CoV-2","lastPublishedDoi":"10.21203/rs.3.rs-4768432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4768432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eSevere respiratory failure often requires intubation and invasive mechanical ventilation. Identifying the factors that lead to this need is crucial, but there are few studies on the evolution of these factors from the onset of symptoms to respiratory failure. This study aims to identify risk factors for invasive mechanical ventilation as well as clinical outcomes in patients with acute respiratory failure considering the time from the onset of symptoms to respiratory failure.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRetrospective cohort study with patients hospitalized between May 1, 2020 and May 1, 2021. Patients over 18 years of age admitted to Intermediate and Intensive Care Units with positive polymerase chain reaction for SARS-CoV-2, chest computed tomography and inflammatory markers performed within 72 hours of admission were included. Patients with chronic obstructive pulmonary disease using home oxygen, intubation not related to Covid-19, heart failure, previous tracheostomy and hospitalization of less than 24 hours were excluded. The main outcome was to identify the factors that determined tracheal intubation and the evolution of these patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 852 patients treated, 302 were excluded, leaving 550, of which 346 required intubation. Intubated patients had a higher body mass index (p\u0026thinsp;=\u0026thinsp;0.02), a higher SAPS-3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a shorter time from symptom onset to hospitalization (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Until the eighth day of hospitalization, these patients had higher levels of C-Reactive Protein (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Interleukin-6 (p\u0026thinsp;=\u0026thinsp;0.003) and D-dimer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chest computed tomography scans revealed a larger area of ​​lung injury since admission. In the Cox model, SAPS-3 (HR\u0026thinsp;=\u0026thinsp;1.028, 95%CI 1.002\u0026ndash;1.055, p\u0026thinsp;=\u0026thinsp;0.038) and time to intubation (HR\u0026thinsp;=\u0026thinsp;1.118, 95%CI 1.021\u0026ndash;1.224, p\u0026thinsp;=\u0026thinsp;0.016) were independent risk factors for mortality. Patients intubated 15 days after the onset of symptoms had a higher risk of mortality (OR\u0026thinsp;=\u0026thinsp;2.13, 95% CI 1.07\u0026ndash;4.23). At intubation, the average respiratory rate was 27.5 breaths per minute, with 85% of FiO2 and ROX index of 4.37. The use of non-invasive ventilatory support was longer in the quartile with more than 15 days until intubation (median of 5 [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] days) and the use of a high-flow nasal cannula was associated with a longer time to decide to intubate (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn patients with Covid-19 and acute respiratory failure, later intubation was associated with higher mortality. Non-invasive ventilatory support strategies can be used as long as there is no delay in using an invasive strategy when necessary.\u003c/p\u003e","manuscriptTitle":"Factors Associated With the Intubation of Patients With Acute Respiratory Failure and Their Impact on Mortality: a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 17:03:17","doi":"10.21203/rs.3.rs-4768432/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-01T07:04:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-31T11:11:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-31T11:08:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-07-19T15:21:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"835f643f-70c8-43fd-b1f8-14460eaf9fef","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:03:19+00:00","versionOfRecord":{"articleIdentity":"rs-4768432","link":"https://doi.org/10.1186/s12890-025-03773-z","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2025-07-03 15:57:44","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2024-09-03 17:03:17","video":"","vorDoi":"10.1186/s12890-025-03773-z","vorDoiUrl":"https://doi.org/10.1186/s12890-025-03773-z","workflowStages":[]},"version":"v1","identity":"rs-4768432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4768432","identity":"rs-4768432","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}