Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic: a matched analysis

Background: The primary aim of our study was to investigate the association between intubation timing and hospital mortality in critically ill patients with COVID-19-associated respiratory failure. We also analysed both the impact of such timing throughout the first four pandemic waves and the influence of prior non-invasive respiratory support on outcomes. Methods: This is a secondary analysis of a multicentre, observational and prospective cohort study that included all consecutive patients undergoing invasive mechanical ventilation due to COVID-19 from across 58 Spanish intensive care units (ICU) participating in the CIBERESUCICOVID project. The study period was between 29 February 2020 and 31 August 2021. Early intubation was defined as that occurring within the first 24 h of intensive care unit (ICU) admission. Propensity score (PS) matching was used to achieve balance across baseline variables between the early intubation cohort and those patients who were intubated after the first 24 h of ICU admission. Differences in outcomes between early and delayed intubation were also assessed. We performed sensitivity analyses to consider a different timepoint (48 h from ICU admission) for early and delayed intubation. Results: Of the 2725 patients who received invasive mechanical ventilation, a total of 614 matched patients were included in the analysis (307 for each group). In the unmatched population, there were no differences in mortality between the early and delayed groups. After PS matching, patients with delayed intubation presented higher hospital mortality (27.3% versus 37.1%, p =0.01), ICU mortality (25.7% versus 36.1%, p=0.007) and 90-day mortality (30.9% versus 40.2%, p=0.02) when compared to the early intubation group. Very similar findings were observed when we used a 48-hour timepoint for early or delayed intubation. The use of early intubation decreased after the first wave of the pandemic (72%, 49%, 46% and 45% in the first, second, third and fourth wave, respectively; first versus second, third and fourth waves p<0.001). In both the main and sensitivity analyses, hospital mortality was lower in patients receiving high-flow nasal cannula (n=294) who were intubated earlier. The subgroup of patients undergoing NIV (n=214) before intubation showed higher mortality when delayed intubation was set as that occurring after 48 h from ICU admission, but not when after 24 h. Conclusions: In patients with COVID-19 requiring invasive mechanical ventilation, delayed intubation was associated with a higher risk of hospital mortality. The use of early intubation significantly decreased throughout the course of the pandemic. Benefits of such an approach occurred more notably in patients who had received high-flow nasal cannula.Financial support was provided by the Instituto de Salud Carlos III de Madrid (COV20/00110, ISCIII), Fondo Europeo de Desarrollo Regional (FEDER), "Una manera de hacer Europa", and the Centro de Investigación Biomedica En Red – Enfermedades Respiratorias (CIBERES). DdGC has received financial support from the Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by European Social Fund (ESF)/”Investing in your future”.Peer ReviewedArticle signat per 70 autors/es: Jordi Riera*1,2; Enric Barbeta*2,3,4; Adrián Tormos5; Ricard Mellado-Artigas2,3; Adrián Ceccato6; Anna Motos4; Laia Fernández-Barat4; Ricard Ferrer1; Darío García-Gasulla5; Oscar Peñuelas7; José Ángel Lorente7; Rosario Menéndez8; Oriol Roca1,2; Andrea Palomeque4,9; Carlos Ferrando2,3; Jordi SoléViolán10; Mariana Novo11; María Victoria Boado12; Luis Tamayo13; Ángel Estella14, Cristóbal Galban15; Josep Trenado16; Arturo Huerta17; Ana Loza18; Luciano Aguilera19; José Luís García Garmendia20; Carme Barberà21; Víctor Gumucio22; Lorenzo Socias23; Nieves Franco24; Luis Jorge Valdivia25; Pablo Vidal26; Víctor Sagredo27; Ángela Leonor Ruiz-García28; Ignacio Martínez Varela29; Juan López30; Juan Carlos Pozo31; Maite Nieto32; José M Gómez33; Aaron Blandino34; Manuel Valledor35; Elena Bustamante-Munguira36; Ángel Sánchez-Miralles37; Yhivian Peñasco38; José Barberán39; Alejandro Ubeda40; Rosario Amaya-Villar41; María Cruz Martín42; Ruth Jorge43; Jesús Caballero44; Judith Marin45; José Manuel Añón46; Fernando Suárez Sipmann47; Guillermo Muñiz2,48;Álvaro Castellanos-Ortega49; Berta Adell-Serrano50; Mercedes Catalán51; Amalia Martínez de la Gándara52; Pilar Ricart53; Cristina Carbajales54; Alejandro Rodríguez55; Emili Díaz6; Mari C de la Torre56; Elena Gallego57; Luisa Cantón-Bulnes58; Nieves Carbonell59, Jessica González60, David de Gonzalo-Calvo60, Ferran Barbé60 and Antoni Torres2,4,9 on behalf of the CiberesUCICOVID Consortium. // 1. Critical Care Department, Hospital Universitari Vall d’Hebron; SODIR, Vall d’Hebron Institut de Recerca, Barcelona, Spain. 2. CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. 3.Surgical Intensive Care Unit, Hospital Clínic de Barcelona, Barcelona, Spain. 4. Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona (UB), Barcelona, Spain. 5. Barcelona Supercomputing Center (BSC), Barcelona, Spain. 6. Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain. Universitat Autonoma de Barcelona (UAB), Spain. 7. Hospital Universitario de Getafe, Universidad Europea, Madrid, Spain. 8. Pneumology Department, Hospital Universitario y Politécnico La Fe/Instituto de Investigación Sanitaria (IIS) La Fe, 46026 Valencia, Spain; Pneumology Department, Hospital Universitario y Politécnico La Fe, Avda, Fernando Abril Martorell 106, 46026 Valencia, Spain. 9.Respiratory Intensive Care Unit, Hospital Clínic de Barcelona, Barcelona, Spain. 10. Critical Care Department, Hospital Dr. Negrín Gran Canaria. Universidad Fernando Pessoa. Las Palmas, Gran Canaria, Spain. 11. Servei de Medicina Intensiva, Hospital Universitari Son Espases, Palma de Mallorca, Illes Balears, Spain. 12. Hospital Universitario de Cruces, Barakaldo, Spain. 13. Critical Care Department, Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain. 14. Departamento Medicina Facultad Medicina Universidad de Cádiz. Hospital Universitario de Jerez, Jerez de la Frontera, Spain. 15. Department of Medicine, CHUS, Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain. 16. Servicio de Medicina Intensiva, Hospital Universitario Mútua de Terrassa, Terrassa, Barcelona, Spain. 17. Pulmonary and Critical Care Division; Emergency Department, Clínica Sagrada Família, Barcelona, Spain. 18. Hospital Virgen de Valme, Sevilla, Spain. 19. Hospital de Basurto, Bilbao, Spain. 20. Intensive Care Unit, Hospital San Juan de Dios del Aljarafe, Bormujos, Sevilla, Spain. 21. Hospital Santa Maria; IRBLleida, Lleida, Spain. 22. Department of Intensive Care. Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain. Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain. 23. Intensive Care Unit, Hospital Son Llàtzer, Palma de Mallorca, Illes Balears, Spain. 24. Hospital Universitario de Móstoles, Madrid, Spain. 25. Hospital Universitario de León, León, Spain. 26. Complexo Hospitalario Universitario de Ourense, Ourense, Spain. 27. Hospital Universitario de Salamanca, Salamanca, Spain. 28. Servicio de Microbiología Clínica, Hospital Universitario Príncipe de Asturias – Departamento de Biomedicina y Biotecnología, Universidad de Alcalá de Henares, Madrid, Spain. 29. Critical Care Department, Hospital Universitario Lucus Augusti, Lugo, Spain. 30. Complejo Asistencial Universitario de Palencia, Palencia, Spain. 31. UGC-Medicina Intensiva, Hospital Universitario Reina Sofia, Instituto Maimonides IMIBIC, Córdoba, Spain. 32. Hospital Universitario de Segovia, Segovia, Spain. 33. Hospital General Universitario Gregorio Marañón, Madrid, Spain. 34. Servicio de Medicina Intensiva, Hospital Universitario Ramón y Cajal, Madrid, Spain. 35. Hospital Universitario "San Agustín", Avilés, Spain. 36. Department of Intensive Care Medicine, Hospital Clínico Universitario Valladolid, Valladolid, Spain. 37. Servicio de Medicina Intensiva. Hospital Universitario Sant Joan d´Alacant, Alicante, Spain. 38. Servicio de Medicina Intensiva, Hospital Universitario Marqués de Valdecilla, Santander, Spain. 39. Hospital Universitario HM Montepríncipe, Universidad San Pablo-CEU, Madrid, Spain. 40. Servicio de Medicina Intensiva, Hospital Punta de Europa, Algeciras, Spain. 41. Intensive Care Clinical Unit, Hospital Universitario Virgen de Rocío, Sevilla, Spain. 42. Hospital Universitario Torrejón- Universidad Francisco de Vitoria, Madrid, Spain. 43. Intensive Care Department, Hospital Nuestra Señora de Gracia, Zaragoza, Spain. 44. Critical Care Department, Hospital Universitari Arnau de Vilanova; IRBLleida, Lleida, Spain. 45. Critical Care Department, Hospital del Mar-IMIM, Barcelona, Spain. 46. Hospital Universitario la Paz, Madrid, Spain. 47. Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain. 48. Departamento de Biología Funcional. Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo; Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Central de Asturias, Oviedo, Spain. 49. Hospital Universitario y Politécnico la Fe, Valencia, Spain. 50. Hospital de Tortosa Verge de la Cinta, Tortosa, Tarragona, Spain. 51. Department of Intensive Care Medicine, Hospital Universitario 12 de Octubre, Madrid, Spain. 52. Hospital Universitario Infanta Leonor, Madrid, Spain. 53. Servei de Medicina Intensiva, Hospital Universitari Germans Trias, Badalona, Spain. 54. Intensive Care Unit, Hospital Álvaro Cunqueiro, Vigo, Spain. 55. Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain. 56. Hospital de Mataró de Barcelona, Spain. 57. Unidad de Cuidados Intensivos, Hospital Universitario San Pedro de Alcántara, Cáceres, Spain. 58. Unidad de Cuidados Intensivos, Hospital Virgen Macarena, Sevilla, Spain. 59. Intensive Care Unit, Hospital Clínico y Universitario de Valencia, Valencia, Spain. 60. Translational Research in Respiratory Medicine, Respiratory Department, Hospital Universitari Aranu de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.Postprint (published version

Occurrence and Effects on Weaning From Mechanical Ventilation of Intensive Care Unit Acquired and Diaphragm Weakness: A Pilot Study

PurposeLimb intensive care unit (ICU)-acquired weakness (ICUAW) and ICU acquired diaphragm weakness (DW) occur frequently in mechanically ventilated (MV) patients; their coexistence in cooperative and uncooperative patients is unknown. This study was designed to (1) describe the co-occurrence of the two conditions (2) evaluate the impact of ICUAW and DW on the ventilator-free days (VFDs) at 28 days and weaning success, and (3) assess the correlation between maximal inspiratory pressure (MIP) and thickening fraction (TFdi) in patients with DW.MethodsThis prospective pilot study was conducted in a single-center on 73 critically ill MV patients. Muscle weakness was defined as a Medical Research Council score &lt; 48 in cooperative patients or a bilateral mean simplified peroneal nerve test &lt; 5.26 mV in uncooperative patients. Diaphragm dysfunction was defined as MIP &lt; 30 cm H2O or as a TFdi &lt; 29%. Weaning success was defined according to weaning according to a new definition (WIND).ResultsFifty-seven patients (78%) had ICUAW and 59 (81%) had DW. The coexistence of the two conditions occurred in 48 patients (65%), without association (χ2 = 1.06, p = 0.304). In the adjusted analysis, ICUAW was independently related to VFDs at 28-days (estimate difference 6 days, p = 0.016), and WIND (OR of 3.62 for having WIND different than short weaning), whereas DW was not. The linear mixed model showed a significant but weak correlation between MIP and TFdi (p &lt; 0.001).ConclusionThis pilot study is the first to explore the coexistence of ICUAW and DW in both cooperative and uncooperative patients; a lack of association was found between DW and ICUAW when considering both cooperative and uncooperative patients. We found a strong correlation between ICUAW but not DW with the VFDs at 28 days and weaning success. A future larger study is warranted in order to confirm our results, and should also investigate the use of transdiaphragmatic twitch pressure measurement during bilateral anterior magnetic phrenic nerve stimulation for the diagnosis of DW

Characteristics and Outcomes in Patients with Ventilator-Associated Pneumonia Who Do or Do Not Develop Acute Respiratory Distress Syndrome. An Observational Study

Ventilator-associated pneumonia (VAP) is a well-known complication of patients on invasive mechanical ventilation. The main cause of acute respiratory distress syndrome (ARDS) is pneumonia. ARDS can occur in patients with community-acquired or nosocomial pneumonia. Data regarding ARDS incidence, related pathogens, and specific outcomes in patients with VAP is limited. This is a cohort study in which patients with VAP were evaluated in an 800-bed tertiary teaching hospital between 2004 and 2016. Clinical outcomes, microbiological and epidemiological data were assessed among those who developed ARDS and those who did not. Forty-one (13.6%) out of 301 VAP patients developed ARDS. Patients who developed ARDS were younger and presented with higher prevalence of chronic liver disease. Pseudomonas aeruginosa was the most frequently isolated pathogen, but without any difference between groups. Appropriate empirical antibiotic treatment was prescribed to ARDS patients as frequently as to those without ARDS. Ninety-day mortality did not significantly vary among patients with or without ARDS. Additionally, patients with ARDS did not have significantly higher intensive care unit (ICU) and 28-day mortality, ICU, and hospital length of stay, ventilation-free days, and duration of mechanical ventilation. In summary, ARDS deriving from VAP occurs in 13.6% of patients. Although significant differences in clinical outcomes were not observed between both groups, further studies with a higher number of patients are needed due to the possibility of the study being underpowered

Automated detection and quantification of reverse triggering effort under mechanical ventilation

Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH0, with a median of 8.7 cmH0. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmHO with important variability between and within patients. BEARDS, NCT03447288

A long-lasting porcine model of ARDS caused by pneumonia and ventilator-induced lung injury

Background: Animal models of acute respiratory distress syndrome (ARDS) do not completely resemble human ARDS, struggling translational research. We aimed to characterize a porcine model of ARDS induced by pneumonia—the most common risk factor in humans—and analyze the additional effect of ventilator-induced lung injury (VILI). Methods: Bronchoscopy-guided instillation of a multidrug-resistant Pseudomonas aeruginosa strain was performed in ten healthy pigs. In six animals (pneumonia-with-VILI group), pulmonary damage was further increased by VILI applied 3 h before instillation and until ARDS was diagnosed by PaO2/FiO2 &lt; 150 mmHg. Four animals (pneumonia-without-VILI group) were protectively ventilated 3 h before inoculum and thereafter. Gas exchange, respiratory mechanics, hemodynamics, microbiological studies and inflammatory markers were analyzed during the 96-h experiment. During necropsy, lobar samples were also analyzed. Results: All animals from pneumonia-with-VILI group reached Berlin criteria for ARDS diagnosis until the end of experiment. The mean duration under ARDS diagnosis was 46.8 ± 7.7 h; the lowest PaO2/FiO2 was 83 ± 5.45 mmHg. The group of pigs that were not subjected to VILI did not meet ARDS criteria, even when presenting with bilateral pneumonia. Animals developing ARDS presented hemodynamic instability as well as severe hypercapnia despite high-minute ventilation. Unlike the pneumonia-without-VILI group, the ARDS animals presented lower static compliance (p = 0.011) and increased pulmonary permeability (p = 0.013). The highest burden of P. aeruginosa was found at pneumonia diagnosis in all animals, as well as a high inflammatory response shown by a release of interleukin (IL)-6 and IL-8. At histological examination, only animals comprising the pneumonia-with-VILI group presented signs consistent with diffuse alveolar damage. Conclusions: In conclusion, we established an accurate pulmonary sepsis-induced ARDS model.</p

A long-lasting porcine model of ARDS caused by pneumonia and ventilator-induced lung injury

Animal models of acute respiratory distress syndrome (ARDS) do not completely resemble human ARDS, struggling translational research. We aimed to characterize a porcine model of ARDS induced by pneumonia-the most common risk factor in humans-and analyze the additional effect of ventilator-induced lung injury (VILI). Bronchoscopy-guided instillation of a multidrug-resistant Pseudomonas aeruginosa strain was performed in ten healthy pigs. In six animals (pneumonia-with-VILI group), pulmonary damage was further increased by VILI applied 3 h before instillation and until ARDS was diagnosed by PaO/FiO < 150 mmHg. Four animals (pneumonia-without-VILI group) were protectively ventilated 3 h before inoculum and thereafter. Gas exchange, respiratory mechanics, hemodynamics, microbiological studies and inflammatory markers were analyzed during the 96-h experiment. During necropsy, lobar samples were also analyzed. All animals from pneumonia-with-VILI group reached Berlin criteria for ARDS diagnosis until the end of experiment. The mean duration under ARDS diagnosis was 46.8 ± 7.7 h; the lowest PaO/FiO was 83 ± 5.45 mmHg. The group of pigs that were not subjected to VILI did not meet ARDS criteria, even when presenting with bilateral pneumonia. Animals developing ARDS presented hemodynamic instability as well as severe hypercapnia despite high-minute ventilation. Unlike the pneumonia-without-VILI group, the ARDS animals presented lower static compliance (p = 0.011) and increased pulmonary permeability (p = 0.013). The highest burden of P. aeruginosa was found at pneumonia diagnosis in all animals, as well as a high inflammatory response shown by a release of interleukin (IL)-6 and IL-8. At histological examination, only animals comprising the pneumonia-with-VILI group presented signs consistent with diffuse alveolar damage. In conclusion, we established an accurate pulmonary sepsis-induced ARDS model. The online version contains supplementary material available at 10.1186/s13054-023-04512-8

SARS-CoV-2-induced Acute Respiratory Distress Syndrome: Pulmonary Mechanics and Gas-Exchange Abnormalities

In January 2020, the first cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were reported in Europe. Multiple outbreaks have since then led to a global pandemic, as well as to massive medical, economic, and social repercussions. SARS-CoV-2 pneumonia can develop into acute respiratory distress syndrome (ARDS) when mechanical ventilation (MV) is needed (3, 4). ARDS produces abnormalities in gas exchange with a variable degree of shunt (5), high dead space ventilation (dead space volume [Vd]/tidal volume [Vt] ratio) (6), diminished pulmonary compliance (7), and alterations to the pulmonary circulation (8). The cornerstone of ARDS management is to provide adequate gas exchange without further lung injury as a result of MV. To date, information regarding the characteristics of SARS-CoV-2-induced ARDS is not completely known. However, this information is crucial to better apply MV and facilitate organ support strategies. We therefore present the characteristics of gas exchange, pulmonary mechanics, and ventilatory management of 50 patients with laboratory-confirmed SARS-CoV-2 infection, who developed ARDS and underwent invasive MV (IMV). Methods: Descriptive analysis included 50 consecutive patients with laboratory-confirmed SARS-CoV-2 infection who developed ARDS (9) and underwent IMV. These patients were admitted to the SARS-CoV-2-dedicated intensive care units (ICUs) at Hospital Clinic of Barcelona, Spain, between March 7 and March 25, 2020. Upon ICU admission, epidemiological characteristics, the severity of SARS-CoV-2 infection with the Acute Physiology and Chronic Health Evaluation II score, prognostic biomarkers of SARS-CoV-2 infection (described in Reference 4), time from hospital to ICU admission, time from ICU admission to intubation, oxygen therapy or noninvasive ventilation (NIV) use, and microbiology were investigated. On the day that criteria for ARDS diagnosis were met (9) and IMV was needed, the following assessments were performed: impairment in oxygenation was analyzed with the partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) ratio, and abnormalities of CO2 metabolism were studied with the ventilatory ratio (VR), a surrogate parameter of Vd/Vt. In addition, adjunctive therapies and MV parameters related with ventilation-induced lung injury (VILI) described elsewhere (11-15) were investigated. Correlations of SARS-CoV-2 prognostic biomarkers (4), pulmonary mechanics, and gas-exchange data were performed. Twenty-eight-day and hospital mortality, ventilator- and ICU-free days at Day 28, hospital and ICU lengths of stay, and need for tracheostomy were also evaluated (16). Finally, a subanalysis assessing differences before and after prone positioning was performed. For additional detail on the method, see the online supplement. Results: By March 25th, 2020, 50 patients with laboratory-confirmed SARS-CoV-2 infection and ARDS had been admitted to our hospital. Table 1 shows the demographic and clinical characteristics of these patients. The median (interquartile range [IQR]) age was 66 (57-74) years. Thirty-six patients (72%) were men. Upon ARDS diagnosis, 44% of patients were initially classified as having moderate ARDS, whereas 24% were classified as having mild ARDS and 32% were classified as having severe ARDS. The outcomes of these patients are shown in Table 1. ICU and hospital lengths of stay were prolonged, and tracheostomy was performed in 30 (60%) patients. Hospital mortality was 34%

Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic : a matched analysis

The primary aim of our study was to investigate the association between intubation timing and hospital mortality in critically ill patients with COVID-19-associated respiratory failure. We also analysed both the impact of such timing throughout the first four pandemic waves and the influence of prior non-invasive respiratory support on outcomes. This is a secondary analysis of a multicentre, observational and prospective cohort study that included all consecutive patients undergoing invasive mechanical ventilation due to COVID-19 from across 58 Spanish intensive care units (ICU) participating in the CIBERESUCICOVID project. The study period was between 29 February 2020 and 31 August 2021. Early intubation was defined as that occurring within the first 24 h of intensive care unit (ICU) admission. Propensity score (PS) matching was used to achieve balance across baseline variables between the early intubation cohort and those patients who were intubated after the first 24 h of ICU admission. Differences in outcomes between early and delayed intubation were also assessed. We performed sensitivity analyses to consider a different timepoint (48 h from ICU admission) for early and delayed intubation. Of the 2725 patients who received invasive mechanical ventilation, a total of 614 matched patients were included in the analysis (307 for each group). In the unmatched population, there were no differences in mortality between the early and delayed groups. After PS matching, patients with delayed intubation presented higher hospital mortality (27.3% versus 37.1%, p =0.01), ICU mortality (25.7% versus 36.1%, p=0.007) and 90-day mortality (30.9% versus 40.2%, p=0.02) when compared to the early intubation group. Very similar findings were observed when we used a 48-hour timepoint for early or delayed intubation. The use of early intubation decreased after the first wave of the pandemic (72%, 49%, 46% and 45% in the first, second, third and fourth wave, respectively; first versus second, third and fourth waves p<0.001). In both the main and sensitivity analyses, hospital mortality was lower in patients receiving high-flow nasal cannula (n=294) who were intubated earlier. The subgroup of patients undergoing NIV (n=214) before intubation showed higher mortality when delayed intubation was set as that occurring after 48 h from ICU admission, but not when after 24 h. In patients with COVID-19 requiring invasive mechanical ventilation, delayed intubation was associated with a higher risk of hospital mortality. The use of early intubation significantly decreased throughout the course of the pandemic. Benefits of such an approach occurred more notably in patients who had received high-flow nasal cannul

Edema Resolution and Clinical Assessment in Poor-Grade Subarachnoid Hemorrhage: Useful Indicators to Predict Delayed Cerebral Infarctions?

Background: The level of consciousness and cerebral edema are among the indicators that best define the intensity of early brain injury following aneurysmal subarachnoid hemorrhage (aSAH). Although these indicators are usually altered in patients with a poor neurological status, their usefulness for selecting patients at risk of cerebral infarction (CI) is not well established. Furthermore, little is known about the evolution of these indicators during the first week of post-ictal events. Our study focused on describing the association of the longitudinal course of these predictors with CI occurrence in patients with severe aSAH. Methods: Out of 265 aSAH patients admitted consecutively to the same institution, 80 patients with initial poor neurological status (WFNS 4–5) were retrospectively identified. After excluding 25 patients with early mortality, a total of 47 patients who underwent early CT (p = 0.001) and in GCS scores (B = 0.32; 95% CI 0.15–0.49; p = 0.001) during the first week. When comparing the ROC curves of Delayed-SEBES vs Early-SEBES as predictors of CI, no significant differences were found (Early-SEBES Area Under the Curve: 0.65; Delayed-SEBES: 0.62; p = 0.17). Additionally, no differences were observed in the relationship between the improvement in the GCS across the first week and the occurrence of CI (p = 0.536). Conclusions: Edema and consciousness level improvement did not seem to be associated with the occurrence of CI in a surviving cohort of patients with severe aSAH. Our results suggest that intensive monitoring should not be reduced in patients with a poor neurological status regardless of an improvement in cerebral edema and level of consciousness during the first week after bleeding