Duration of the first prone positioning maneuver and its association with 90-day mortality in patients with acute respiratory failure due to COVID-19: A retrospective study of time terciles.

OBJECTIVE: To investigate the association between the duration of the first prone positioning maneuver (PPM) and 90-day mortality in patients with C-ARDS. DESIGN: Retrospective, observational, and analytical study. SETTING: COVID-19 ICU of a tertiary hospital. PATIENTS: Adults over 18 years old, with a confirmed diagnosis of SARS-CoV-2 disease requiring PPM. INTERVENTIONS: Multivariable analysis of 90-day survival. MAIN VARIABLES OF INTEREST: Duration of the first PPM, number of PPM sessions, 90-day mortality. RESULTS: 271 patients undergoing PPM were analyzed: first tertile (n = 111), second tertile (n = 95) and third tertile (n = 65). The results indicated that the median duration of PDP was 14 h (95% CI: 10-16 h) in the first tertile, 19 h (95% CI: 18-20 h) in the second tertile and 22 h (95% CI: 21-24 h) in the third tertile. Comparison of survival curves using the Logrank test did not reach statistical significance (p = 0.11). Cox Regression analysis showed an association between the number of pronation sessions (patients receiving between 2 and 5 sessions (HR = 2.19; 95% CI: 1.07-4.49); and those receiving more than 5 sessions (HR = 6.05; 95% CI: 2.78-13.16) and 90-day mortality. CONCLUSIONS: while the duration of PDP does not appear to significantly influence 90-day mortality, the number of pronation sessions is identified as a significant factor associated with an increased risk of mortality.

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

BACKGROUND: Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS: Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS: Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS: During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

El método del factor de Bayes en el análisis de la energía mecánica en pacientes con insuficiencia respiratoria grave por SARS-CoV-2 / The Bayes factor in the analysis of mechanical power in patients with severe respiratory failure due to SARS-CoV-2

Objetivo: Precisar el grado de fuerza probatoria de las hipótesis estadísticas con relación a la mortalidad a 28 días y el valor umbral de 17J/min de potencia mecánica (PM) en pacientes con insuficiencia respiratoria secundaria a SARS-CoV-2. Diseño: Estudio de cohortes, longitudinal y analítico. Ámbito: Unidad de cuidados intensivos de un hospital de tercer nivel en España. Pacientes: Enfermos ingresados por infección por SARS-CoV-2 con ingreso en la UCI entre marzo de 2020 y marzo de 2022. Intervenciones: Análisis bayesiano con el modelo binomial beta. Variables de interés principales: Factor de Bayes, mechanical power. Resultados: Fueron analizados 253 pacientes. La frecuencia respiratoria inicial (BF10: 3,83×106), el valor de la presión pico (BF10: 3,72×1013) y el desarrollo de neumotórax (BF10: 17.663) fueron los valores con más probabilidad de ser diferentes entre los 2 grupos de pacientes comparados. En el grupo de pacientes con PM<17J/min se estableció un BF10 de 12,71 y un BF01 de 0,07 con un IdC95% de 0,27-0,58; Para el grupo de pacientes con PM≥17J/min el BF10 fue de 36.100 y el BF01 de 2,77e-05 con un IdC95% de 0,42-0,72. Conclusiones: Un valor de PM≥17J/min se asocia con una evidencia extrema con la mortalidad a 28 días en pacientes que necesitaron ventilación mecánica por insuficiencia respiratoria secundaria a enfermedad por SARS-CoV-2. (AU)

Objective: To specify the degree of probative force of the statistical hypotheses in relation to mortality at 28 days and the threshold value of 17J/min mechanical power (MP) in patients with respiratory failure secondary to SARS-CoV-2. Design: Cohort study, longitudinal, analytical. Setting: Intensive care unit of a third level hospital in Spain. Patients: Patients admitted for SARS-CoV-2 infection with admission to the ICU between March 2020 and March 2022. Interventions: Bayesian analysis with the beta binomial model. Main variables of interest: Bayes factor, mechanical power. Results: A total of 253 patients were analyzed. Baseline respiratory rate (BF10: 3.83×106), peak pressure value (BF10: 3.72×1013) and neumothorax (BF10: 17,663) were the values most likely to be different between the two groups of patients compared. In the group of patients with MP<17J/min, a BF10 of 12.71 and a BF01 of 0.07 were established with an 95%CI of 0.27-0.58. For the group of patients with MP≥17J/min the BF10 was 36,100 and the BF01 of 2.77e-05 with an 95%CI of 0.42-0.72. Conclusions: A MP≥17J/min value is associated with extreme evidence with 28-day mortality in patients requiring MV due to respiratory failure secondary to SARS-CoV-2 disease. (AU)

Estudio retrospectivo del efecto de la vacunación frente al SARS-CoV-2 en enfermos graves que ingresan en una unidad de cuidados intensivos / Retrospective study of the effect of vaccination against SARS-CoV-2 in seriously ill patients admitted to an intensive care unit

Antecedentes y objetivo Planteamos nuestro trabajo con el objetivo de comparar las características clínico-epidemiológicas, la estancia en la UCI y la mortalidad de pacientes con COVID-19 que ingresaron en la UCI con vacunación completa, incompleta o sin vacunar. Pacientes y métodos Estudio retrospectivo de cohortes (marzo 2020-marzo 2022). Los pacientes fueron clasificados en pacientes no vacunados, pauta de vacunación completa y pauta de vacunación incompleta. Se realizó inicialmente un análisis descriptivo de la muestra, un análisis multivariable de la supervivencia ajustando un modelo de regresión de Cox y un análisis de supervivencia a 90 días con el método de Kaplan-Meier para la variable de tiempo de muerte. Resultados Fueron analizados los 894 pacientes: 179 con una pauta de vacunación completa, 32 con una pauta incompleta y 683 no estaban vacunados. Los enfermos vacunados presentaron con menor frecuencia (10 vs. 21% y 18%) un SDRA grave. La curva de supervivencia no presentó diferencias en la probabilidad de sobrevivir a los 90 días entre los grupos estudiados (p = 0,898). En el análisis de regresión de COX, únicamente la necesidad de VM durante el ingreso y el valor de LDH (por unidad de medida) en las primeras 24 h de ingreso se asociaron de forma significativa con la mortalidad a los 90 días (HR: 5,78; IC 95%: 1,36-24,48); p = 0,01 y HR: 1,01; IC 95%: 1,00-1,02; p = 0,03, respectivamente. Conclusiones Los pacientes vacunados frente a la COVID-19 con enfermedad grave por SARS-CoV-2 presentan unas tasas de SDRA grave y de VM menores que las de aquellos pacientes no vacunados (AU)

Background and objective Our study aims to compare the clinical and epidemiological characteristics, length of stay in the ICU, and mortality rates of COVID-19 patients admitted to the ICU who are fully vaccinated, partially vaccinated, or unvaccinated. Patients and methods Retrospective cohort study (March 2020-March 2022). Patients were classified into unvaccinated, fully vaccinated, and partially vaccinated groups. We initially performed a descriptive analysis of the sample, a multivariable survival analysis adjusting for a Cox regression model, and a 90-day survival analysis using the Kaplan-Meier method for the death time variable. Results A total of 894 patients were analyzed: 179 with full vaccination, 32 with incomplete vaccination, and 683 were unvaccinated. Vaccinated patients had a lower incidence (10% vs. 21% and 18%) of severe ARDS. The survival curve did not show any differences in the probability of surviving for 90 days among the studied groups (p = 0.898). In the Cox regression analysis, only the need for mechanical ventilation during admission and the value of LDH (per unit of measurement) in the first 24 hours of admission were significantly associated with mortality at 90 days (HR: 5.78; 95% CI: 1.36-24.48); p = 0.01 and HR: 1.01; 95% CI: 1.00-1.02; p = 0.03, respectively. Conclusions Patients with severe SARS-CoV-2 disease who are vaccinated against COVID-19 have a lower incidence of severe ARDS and mechanical ventilation than unvaccinated patients (AU)

Análisis de las características de los pacientes mayores que ingresaron en una unidad de cuidados intensivos durante las 6 olas de la pandemia por SARS-CoV-2: implicaciones para la atención médica / Analysis of characteristics of elderly patients admitted to an intensive care unit during six waves of the SARS-CoV-2 pandemic: Implications for medical care

Objetivo Analizar las características de los enfermos adultos graves de mayor edad, durante las 6 olas de la pandemia COVID-19. Método Estudio retrospectivo, observacional y analítico sobre pacientes mayores de 70 años con ingreso en la UCI (marzo-2020/marzo-2022). Los pacientes se categorizaron en 3 grupos en función de la edad: 70-74 años, 75-79 años y >80 años. Se realizó inicialmente un análisis descriptivo y comparativo de la muestra, y un análisis de supervivencia a los 28, 60 y 90 días con el método de Kaplan-Meier. El análisis multivariable de la supervivencia se realizó ajustando un modelo de Cox. Resultados De 301 enfermos, el menor número de ingresos se produjo durante la primera ola (20 [6%]), frente a la que fue la ola con mayor número de ingresos: la sexta ola (76 [25%]). Las curvas de supervivencia a los 28, a los 60 días y a los 90 días evidenciaron una mayor probabilidad de sobrevivir en los grupos de menor edad (p<0,01 y p=0,01, respectivamente). La troponina al ingreso (por unidad, ng/l), evidenció un asociación significativa con la mortalidad a 28 y 60 días (HR: 1,00; IC 95%: 1,00-1,01; p<0,05). Tomando como referencia la 1.ª oleada de la pandemia, el ingreso en 3.ª oleada se comportó como un factor de protección frente a la mortalidad a los 28 y 60 días de seguimiento (HR: 0,18; IC 95%: 0,02-0,64; p<0,05; HR: 0,13; IC 95%: 0,02-0,64; p<0,05, respectivamente). Conclusiones El momento de ingreso y biomarcadores, como la troponina, se constituyen en marcadores pronósticos independientes de la edad en la población añosa (AU)

Objective To analyze the characteristics of seriously ill elderly patients during the six waves of the COVID-19 pandemic. Method Retrospective, observational and analytical study of patients over 70 years of age admitted to the ICU (March-2020 to March-2022). Patients were categorized into three groups based on age: 70-74 years; 75-79 years; and >80 years. A descriptive and comparative analysis of the sample was initially performed; and a 28-, 60- and 90-day survival analysis using the Kaplan–Meier method. Multivariate survival analysis was performed by fitting a Cox model. Results Of 301 patients, the lowest number of admissions occurred during the first wave (20 (6%)), compared to the wave with the highest number of admissions: the sixth wave (76 (25%)). The survival curves at 28 days, 60 days and 90 days showed a higher probability of survival in the younger age groups (P<.01 and P=.01, respectively). Troponin at admission (per unit, ng/l) showed a significant association with 28- and 60-day mortality (HR: 1.00; 95% CI: 1.00-1.01; P<.05). Taking the 1st wave of the pandemic as a reference, admission in the 3rd wave behaved as a protective factor against mortality at 28 and 60 days of follow-up (HR: 0.18; 95% CI: 0.02-0.64; P<.05; HR: 0.13; 95% CI: 0.02–0.64; P<.05, respectively). Conclusions The time of admission and biomarkers, such as troponin, constitute prognostic markers independent of age in the elderly population (AU)

[Analysis of characteristics of elderly patients admitted to an intensive care unit during six waves of the SARS-CoV-2 pandemic: Implications for medical care]. / Análisis de las características de los pacientes mayores que ingresaron en una unidad de cuidados intensivos durante las 6 olas de la pandemia por SARS-CoV-2: implicaciones para la atención médica.

OBJECTIVE: To analyze the characteristics of seriously ill elderly patients during the six waves of the COVID-19 pandemic. METHOD: Retrospective, observational and analytical study of patients over 70 years of age admitted to the ICU (March-2020 to March-2022). Patients were categorized into three groups based on age: 70-74 years; 75-79 years; and >80 years. A descriptive and comparative analysis of the sample was initially performed; and a 28-, 60- and 90-day survival analysis using the Kaplan-Meier method. Multivariate survival analysis was performed by fitting a Cox model. RESULTS: Of 301 patients, the lowest number of admissions occurred during the first wave (20 (6%)), compared to the wave with the highest number of admissions: the sixth wave (76 (25%)). The survival curves at 28 days, 60 days and 90 days showed a higher probability of survival in the younger age groups (P<.01 and P=.01, respectively). Troponin at admission (per unit, ng/l) showed a significant association with 28- and 60-day mortality (HR: 1.00; 95% CI: 1.00-1.01; P<.05). Taking the 1st wave of the pandemic as a reference, admission in the 3rd wave behaved as a protective factor against mortality at 28 and 60 days of follow-up (HR: 0.18; 95% CI: 0.02-0.64; P<.05; HR: 0.13; 95% CI: 0.02-0.64; P<.05, respectively). CONCLUSIONS: The time of admission and biomarkers, such as troponin, constitute prognostic markers independent of age in the elderly population.

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study.

BACKGROUND: The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. METHODS: This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. RESULTS: Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. CONCLUSIONS: A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

[The Bayes factor in the analysis of mechanical power in patients with severe respiratory failure due to SARS-CoV-2]. / El método del factor de Bayes en el análisis de la energía mecánica en pacientes con insuficiencia respiratoria grave por SARS-CoV-2.

Objective: To specify the degree of probative force of the statistical hypotheses in relation to mortality at 28 days and the threshold value of 17 J/min mechanical power (MP) in patients with respiratory failure secondary to SARS-CoV-2. Design: Cohort study, longitudinal, analytical. Setting: Intensive care unit of a third level hospital in Spain. Patients: Patients admitted for SARS-CoV-2 infection with admission to the ICU between March 2020 and March 2022. Interventions: Bayesian analysis with the beta binomial model. Main variables of interest: Bayes factor, mechanical power. Results: A total of 253 patients were analyzed. Baseline respiratory rate (BF10: 3.83 × 106), peak pressure value (BF10: 3.72 × 1013) and neumothorax (BF10: 17,663) were the values most likely to be different between the two groups of patients compared. In the group of patients with MP < 17 J/min, a BF10 of 12.71 and a BF01 of 0.07 were established with an 95%CI of 0.27-0.58. For the group of patients with MP ≥ 17 J/min the BF10 was 36,100 and the BF01 of 2.77e-05 with an 95%CI of 0.42-0.72. Conclusions: A MP ≥ 17 J/min value is associated with extreme evidence with 28-day mortality in patients requiring MV due to respiratory failure secondary to SARS-CoV-2 disease.

[Retrospective study of the effect of vaccination against SARS-CoV-2 in seriously ill patients admitted to an intensive care unit]. / Estudio retrospectivo del efecto de la vacunación frente al SARS-CoV-2 en enfermos graves que ingresan en una unidad de cuidados intensivos.

BACKGROUND AND OBJECTIVE: Our study aims to compare the clinical and epidemiological characteristics, length of stay in the ICU, and mortality rates of COVID-19 patients admitted to the ICU who are fully vaccinated, partially vaccinated, or unvaccinated. PATIENTS AND METHODS: Retrospective cohort study (March 2020-March 2022). Patients were classified into unvaccinated, fully vaccinated, and partially vaccinated groups. We initially performed a descriptive analysis of the sample, a multivariable survival analysis adjusting for a Cox regression model, and a 90-day survival analysis using the Kaplan-Meier method for the death time variable. RESULTS: A total of 894 patients were analyzed: 179 with full vaccination, 32 with incomplete vaccination, and 683 were unvaccinated. Vaccinated patients had a lower incidence (10% vs. 21% and 18%) of severe ARDS. The survival curve did not show any differences in the probability of surviving for 90 days among the studied groups (p = 0.898). In the Cox regression analysis, only the need for mechanical ventilation during admission and the value of LDH (per unit of measurement) in the first 24 hours of admission were significantly associated with mortality at 90 days (HR: 5.78; 95% CI: 1.36-24.48); p = 0.01 and HR: 1.01; 95% CI: 1.00-1.02; p = 0.03, respectively. CONCLUSIONS: Patients with severe SARS-CoV-2 disease who are vaccinated against COVID-19 have a lower incidence of severe ARDS and mechanical ventilation than unvaccinated patients.

The Bayes factor in the analysis of mechanical power in patients with severe respiratory failure due to SARS-CoV-2.

OBJECTIVE: To specify the degree of probative force of the statistical hypotheses in relation to mortality at 28 days and the threshold value of 17 J/min mechanical power (MP) in patients with respiratory failure secondary to SARS-CoV-2. DESIGN: Cohort study, longitudinal, analytical. SETTING: Intensive care unit of a third level hospital in Spain. PATIENTS: Patients admitted for SARS-CoV-2 infection with admission to the ICU between March 2020 and March 2022. INTERVENTIONS: Bayesian analysis with the beta binomial model. MAIN VARIABLES OF INTEREST: Bayes factor, mechanical power. RESULTS: A total of 253 patients were analyzed. Baseline respiratory rate (BF10: 3.83 × 106), peak pressure value (BF10: 3.72 × 1013) and neumothorax (BF10: 17,663) were the values most likely to be different between the two groups of patients compared. In the group of patients with MP < 17 J/min, a BF10 of 12.71 and a BF01 of 0.07 were established with an 95%CI of 0.27-0.58. For the group of patients with MP ≥ 17 J/min the BF10 was 36,100 and the BF01 of 2.77e-05 with an 95%CI of 0.42-0.72. CONCLUSIONS: A MP ≥ 17 J/min value is associated with extreme evidence with 28-day mortality in patients requiring MV due to respiratory failure secondary to SARS-CoV-2 disease.

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients.

INTRODUCTION: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. METHODS: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. RESULTS: The median [p25-p75] time from discharge to follow-up was 3.57 [2.77-4.92] months. Median age was 60 [53-67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO<80% and 24% having DLCO<60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO<60% were chronic lung disease (CLD) (OR: 1.86 (1.18-2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37-1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18-1.63)), urea (OR: 1.16 (0.97-1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73-1.06)). Bacterial pneumonia (1.62 (1.11-2.35)) and duration of ventilation (NIMV (1.23 (1.06-1.42), IMV (1.21 (1.01-1.45)) and prone positioning (1.17 (0.98-1.39)) were associated with fibrotic lesions. CONCLUSION: Age and CLD, reflecting patients' baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities.

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 coronavirus disease 2019 (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 noninvasive 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 (ICUs) 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 ICU admission. Propensity score matching was used to achieve a 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 time-point (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 propensity score 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) compared with the early intubation group. Very similar findings were observed when we used a 48-h time-point 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 waves, 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 (HFNC) (n=294) who were intubated earlier. The subgroup of patients undergoing noninvasive ventilation (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 HFNC.

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study.

Background: The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods: Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings: Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation: Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Funding: ISCIII, UNESPA, CIBERES, FEDER, ESF.