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.

[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.

Chronic critical illness after trauma injury: outcomes and experience in a trauma center.

OBJECTIVE: To determine the prevalence, risk factors and functional results of chronic critical illness (CCI) in polytrauma patients. DESIGN: Single-center observational retrospective study. SETTING: ICU at a tertiary hospital in Santander, Spain, between 2015 and 2019. PATIENTS: Adult trauma patients who survived beyond 48 h after injury. CCI was defined as the need for mechanical ventilation for at least 14 days or tracheostomy for difficult weaning. MEASUREMENTS AND MAIN RESULTS: About 62/575 developed CCI. These patients were characterized by higher ISS score [17 (SD 10) vs. 13.8 (SD 8.2); p < 0.001] and higher NISS (26 (SD 11) vs. 19.2 (SD 10.5); p = 0.001). CCI group had greater proportion of hospital-acquired infections (100% vs. 18.1%; p < 0.001), and acute kidney failure (33.9% vs. 22.8% p < 0.001). During the first 24 h of admission, CCI group required in a greater proportion surgical intervention (50% vs. 29%; p = 0.001), and blood products (31.3% vs. 20.5%; p < 0.047). Hospital ward stay was longer in CCI patients [9.5 days (IQR 5-16.9) vs. 43.9 (IQR 30.3-53) p < 0.001]. The CCI mortality was higher (19.5% vs. 8.1%; p = 0.004). Surgical intervention in the first 24 h (OR 2.5 95% CI 1.1-4.1), age (> 55 years) (OR 2.1 95%CI 1.1-4.2), ISS score (OR 1.1 95%CI 1.02-1.3), GCS score (OR 0.8 95%CI 0.4-23.2) and multiple organ failure (OR 9.5 95%CI 3.9-23.2) were predictors of CCI in the multivariate analysis. CONCLUSIONS: CCI after severe trauma appears in a considerable proportion of patients. Early identification and implementation of specific interventions could change the evolution of this process.

Management of hyperinflammation in COVID-19 patients

In response to SARS-CoV-2 infection, the immune system physiologically upregulates to try to clear the virus from the body; failure to compensate for this inflammatory response with an anti-inflammatory response leads to dysregulation of the immune system that ultimately leads to a situation of uncontrolled hyperinflammation called cytokine storm. This cytokine storm can cause ARDS or multi-organ failure leading to patient death. This review exposes the different mechanisms of the inflammatory response in COVID-19 infection and the therapeutic options to treat this process. (AU)