Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: A CHAID decision-tree analysis

Objectives: To define which variables upon ICU admission could be related to the presence of coinfection using CHAID (Chi-squared Automatic Interaction Detection) analysis. Methods: A secondary analysis from a prospective, multicentre, observational study (2009-2014) in ICU patients with confirmed A(H1N1)pdm09 infection. We assessed the potential of biomarkers and clinical variables upon admission to the ICU for coinfection diagnosis using CHAID analysis. Performance of cut-off points obtained was determined on the basis of the binominal distributions of the true (+) and true (−) results. Results: Of the 972 patients included, 196 (20.3%) had coinfection. Procalcitonin (PCT; ng/mL 2.4 vs. 0.5, p < 0.001), but not C-reactive protein (CRP; mg/dL 25 vs. 38.5; p = 0.62) was higher in patients with coinfection. In CHAID analyses, PCT was the most important variable for coinfection. PCT <0.29 ng/mL showed high sensitivity (Se = 88.2%), low Sp (33.2%) and high negative predictive value (NPV = 91.9%). The absence of shock improved classification capacity. Thus, for PCT <0.29 ng/mL, the Se was 84%, the Sp 43% and an NPV of 94% with a post-test probability of coinfection of only 6%. Conclusion: PCT has a high negative predictive value (94%) and lower PCT levels seems to be a good tool for excluding coinfection, particularly for patients without shock

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Spanish Influenza Score (SIS): utilidad del Machine Learning en el desarrollo de una escala temprana de predicción de mortalidad en la gripe grave

11 páginasObjetivo Desarrollar una escala predicitva de mortalidad (SIS) en pacientes con gripe grave considerando las variables de ingreso a UCI y comparar su eficacia respecto de un modelo d Random Forest (RF). Diseño Sub-análisis de base de datos GETGAG/SEMICYUC. Ámbito Medicina intensiva. Intervenciones Ninguna. Pacientes Pacientes ingresados en 184 UCI españolas (2009-2018) con infección por gripe. Variables: Demográficas, nivel de gravedad, tiempo síntomas hasta el ingreso al hospital (Gap-H) o desde hospital a UCI (Gap-UCI), o al diagnóstico (Gap-Dg), vacunación, cuadrantes infiltrados, insuficiencia renal, ventilación no-invasiva o invasiva (VM), shock y comorbilidades. Los puntos de corte y la importancia de las variables se obtuvieron de forma automática. Se realizó validación cruzada y regresión logística a partir de la cual se desarrolló la puntuación SIS. Se aplicó la puntuación y se calculó la exactitud y la discriminación (AUC-ROC) para SIS y RF. El análisis se realizó mediante CRAN-R Project. Resultados Se incluyeron 3.959 pacientes, edad 55 (43-67) años, 60% hombres, APACHE II de 16 (12-21) y SOFA 5 (4-8) puntos y una mortalidad del 21,3%. VM, shock, APACHE II, SOFA, insuficiencia renal aguda y Gap-UCI fueron incluidas en SIS. A partir de los OR se construyó el SIS que demostró una exactitud del 83% y un AUC-ROC del 82% similar al RF (AUC-ROC 82%). Conclusiones La escala SIS de fácil aplicación, ha demostrado una adecuada capacidad de estratificación del riesgo de mortalidad en la UCI. Sin embargo, estos resultados deberán ser validados prospectivamente

Delay in diagnosis of influenza A (H1N1)pdm09 virus infection in critically ill patients and impact on clinical outcome

Background: Patients infected with influenza A (H1N1)pdm09 virus requiring admission to the ICU remain an important source of mortality during the influenza season. The objective of the study was to assess the impact of a delay in diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection on clinical outcome in critically ill patients admitted to the ICU. Methods: A prospective multicenter observational cohort study was based on data from the GETGAG/SEMICYUC registry (2009–2015) collected by 148 Spanish ICUs. All patients admitted to the ICU in which diagnosis of influenza A (H1N1)pdm09 virus infection had been established within the first week of hospitalization were included. Patients were classified into two groups according to the time at which the diagnosis was made: early (within the first 2 days of hospital admission) and late (between the 3rd and 7th day of hospital admission). Factors associated with a delay in diagnosis were assessed by logistic regression analysis. Results: In 2059 ICU patients diagnosed with influenza A (H1N1)pdm09 virus infection within the first 7 days of hospitalization, the diagnosis was established early in 1314 (63.8 %) patients and late in the remaining 745 (36.2 %). Independent variables related to a late diagnosis were: age (odds ratio (OR) = 1.02, 95 % confidence interval (CI) 1.01–1.03, P < 0.001); first seasonal period (2009–2012) (OR = 2.08, 95 % CI 1.64–2.63, P < 0.001); days of hospital stay before ICU admission (OR = 1.26, 95 % CI 1.17–1.35, P < 0.001); mechanical ventilation (OR = 1.58, 95 % CI 1.17–2.13, P = 0.002); and continuous venovenous hemofiltration (OR = 1.54, 95 % CI 1.08–2.18, P = 0.016). The intra-ICU mortality was significantly higher among patients with late diagnosis as compared with early diagnosis (26.9 % vs 17.1 %, P < 0.001). Diagnostic delay was one independent risk factor for mortality (OR = 1.36, 95 % CI 1.03–1.81, P < 0.001). Conclusions: Late diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection is associated with a delay in ICU admission, greater possibilities of respiratory and renal failure, and higher mortality rate. Delay in diagnosis of flu is an independent variable related to death

Pacientes con Gripe por Virus Influenza A (H1N1)pdm09 ingresados en UCI. Impacto de las Recomendaciones de la SEMICYUC

OBJECTIVES: To evaluate the impact of the recommendations of the SEMICYUC (2012) on severe influenza A. DESIGN: A prospective multicenter observational study was carried out. SETTING: ICU. PATIENTS: Patients infected with severe influenza A (H1N1) from the GETGAG/SEMICYUC registry. INTERVENTIONS: Analysis of 2 groups according to the epidemic period of the diagnosis (2009-2011; 2013-2015). VARIABLES: Demographic, temporal, comorbidities, severity, treatments, mortality, late diagnosis and place of acquisition. RESULTS: A total of 2,205 patients were included, 1,337 (60.6%) in the first period and 868 (39.4%) in the second one. Age and severity on admission were significantly greater in the second period, as well as co-infection. With regard to the impact of the recommendations, in the second period the diagnosis was established earlier (70.8 vs. 61.1%, P<.001), without changes in the start of treatment. Patients received less corticosteroid treatment (39.7 vs. 44.9%, P<.05), more NIMV was used (47.4 vs. 33.2%, P<.001) and more vaccination was made (11.1 vs. 1.7%, P<.001), without changes in mortality (24.2 vs. 20.7%). A decrease in nosocomial infection was also noted (9.8 vs. 16%, P<.001). Patients needed less MV with more days of ventilation, more vasopressor drug use and more ventral decubitus. CONCLUSIONS: The management of patients with severe influenza A (H1N1) has changed over the years, though without changes in mortality. The recommendations of the SEMICYUC (2012) have allowed earlier diagnosis and improved corticosteroid use. Pending challenges are the delay in treatment, the vaccination rate and the use of NIMV

Prior influenza vaccine is not a risk factor for bacterial coinfection in patients admitted to the ICU due to severe influenza

20.500.12530/87855Objective: To determine whether the prior usage of the flu vaccine is a risk factor for bacterial co-infection in patients with severe influenza.Design: This was a retrospective observational cohort study of subjects admitted to the ICU. A propensity score matching, and logistic regression adjusted for potential confounders were carried out to evaluate the association between prior influenza vaccination and bacterial coinfection.Settings: 184 ICUs in Spain due to severe influenza.Patients: Patients included in the Spanish prospective flu registry.Interventions: Flu vaccine prior to the hospital admission.Results: A total of 4175 subjects were included in the study. 489 (11.7%) received the flu vaccine prior to develop influenza infection. Prior vaccinated patients were older 71 [61-78], andpredominantly male 65.4%, with at least one comorbid condition 88.5%. Prior vaccination wasnot associated with bacterial co-infection in the logistic regression model (OR: 1.017; 95%CI0.803-1.288; p = 0.885). After matching, the average treatment effect of prior influenza vaccine on bacterial co-infection was not statistically significant when assessed by propensity scorematching (p = 0.87), nearest neighbor matching (p = 0.59) and inverse probability weighting( p = 0.99).Conclusions: No association was identified between prior influenza vaccine and bacterial coinfection in patients admitted to the ICU due to severe influenza. Post influenza vaccinationstudies are necessary to continue evaluating the possible benefits. (c) 2021 Elsevier Espana, S.L.U. y SEMICYUC. All rights reserved

Prior influenza vaccine is not a risk factor for bacterial coinfection in patients admitted to the ICU due to severe influenza

10 páginasObjective To determine whether the prior usage of the flu vaccine is a risk factor for bacterial co-infection in patients with severe influenza. Design This was a retrospective observational cohort study of subjects admitted to the ICU. A propensity score matching, and logistic regression adjusted for potential confounders were carried out to evaluate the association between prior influenza vaccination and bacterial co-infection. Settings 184 ICUs in Spain due to severe influenza. Patients Patients included in the Spanish prospective flu registry. Interventions Flu vaccine prior to the hospital admission. Results A total of 4175 subjects were included in the study. 489 (11.7%) received the flu vaccine prior to develop influenza infection. Prior vaccinated patients were older 71 [61–78], and predominantly male 65.4%, with at least one comorbid condition 88.5%. Prior vaccination was not associated with bacterial co-infection in the logistic regression model (OR: 1.017; 95%CI 0.803–1.288; p = 0.885). After matching, the average treatment effect of prior influenza vaccine on bacterial co-infection was not statistically significant when assessed by propensity score matching ( p = 0.87), nearest neighbor matching ( p = 0.59) and inverse probability weighting ( p = 0.99). Conclusions No association was identified between prior influenza vaccine and bacterial coinfection in patients admitted to the ICU due to severe influenza. Post influenza vaccination studies are necessary to continue evaluating the possible benefits.Objetivo Determinar si el uso previo de la vacuna antigripal es un factor de riesgo para coinfección bacteriana en pacientes con influenza grave. Diseño Este fue un estudio de cohorte observacional retrospectivo de sujetos ingresados en la UCI. Se realizó un emparejamiento por puntuación de propensión y una regresión logística ajustada para posibles factores de confusión para evaluar la asociación entre el antecedente de vacunación contra la gripe y la coinfección bacteriana. Ámbito Ciento ochenta y cuatro ingresos en UCI españolas por gripe grave. Pacientes Pacientes incluidos en el registro prospectivo español de gripe. Intervenciones Vacuna antigripal previa al ingreso hospitalario. Resultados Se incluyó en el estudio un total de 4.175 sujetos. Recibieron la vacuna contra la influenza antes de desarrollar la infección por influenza 489 (11,7%). Los pacientes previamente vacunados eran mayores de 71 años (RIC 61-78), predominantemente varones (65,4%) y con al menos una condición comórbida (88,5%). La vacunación previa no se asoció con la coinfección bacteriana en el modelo de regresión logística (OR: 1,017; IC95% 0,803-1,288; p = 0,885). Después del emparejamiento, el efecto promedio del tratamiento del antecedente de vacuna contra la influenza sobre la coinfección bacteriana no fue estadísticamente significativo cuando se evaluó mediante el emparejamiento por puntuación de propensión (p = 0,87), por emparejamiento del vecino más cercano (p = 0,59) y mediante la ponderación de probabilidad inversa (p = 0,99). Conclusiones No se identificó asociación entre el antecedente de vacuna antigripal y coinfección bacteriana en pacientes ingresados en UCI por influenza severa. Más estudios para evaluar los efectos de la vacunación contra la gripe son necesarios para continuar evaluando los posibles beneficios

Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: A CHAID decision-tree analysis.

To define which variables upon ICU admission could be related to the presence of coinfection using CHAID (Chi-squared Automatic Interaction Detection) analysis. A secondary analysis from a prospective, multicentre, observational study (2009-2014) in ICU patients with confirmed A(H1N1)pdm09 infection. We assessed the potential of biomarkers and clinical variables upon admission to the ICU for coinfection diagnosis using CHAID analysis. Performance of cut-off points obtained was determined on the basis of the binominal distributions of the true (+) and true (-) results. Of the 972 patients included, 196 (20.3%) had coinfection. Procalcitonin (PCT; ng/mL 2.4 vs. 0.5, p  PCT has a high negative predictive value (94%) and lower PCT levels seems to be a good tool for excluding coinfection, particularly for patients without shock

Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: A CHAID decision-tree analysis

Objectives: To define which variables upon ICU admission could be related to the presence of coinfection using CHAID (Chi-squared Automatic Interaction Detection) analysis. Methods: A secondary analysis from a prospective, multicentre, observational study (2009-2014) in ICU patients with confirmed A(H1N1)pdm09 infection. We assessed the potential of biomarkers and clinical variables upon admission to the ICU for coinfection diagnosis using CHAID analysis. Performance of cut-off points obtained was determined on the basis of the binominal distributions of the true (+) and true (−) results. Results: Of the 972 patients included, 196 (20.3%) had coinfection. Procalcitonin (PCT; ng/mL 2.4 vs. 0.5, p < 0.001), but not C-reactive protein (CRP; mg/dL 25 vs. 38.5; p = 0.62) was higher in patients with coinfection. In CHAID analyses, PCT was the most important variable for coinfection. PCT <0.29 ng/mL showed high sensitivity (Se = 88.2%), low Sp (33.2%) and high negative predictive value (NPV = 91.9%). The absence of shock improved classification capacity. Thus, for PCT <0.29 ng/mL, the Se was 84%, the Sp 43% and an NPV of 94% with a post-test probability of coinfection of only 6%. Conclusion: PCT has a high negative predictive value (94%) and lower PCT levels seems to be a good tool for excluding coinfection, particularly for patients without shock

Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: A CHAID decision-tree analysis

Objectives: To define which variables upon ICU admission could be related to the presence of coinfection using CHAID (Chi-squared Automatic Interaction Detection) analysis. Methods: A secondary analysis from a prospective, multicentre, observational study (2009-2014) in ICU patients with confirmed A(H1N1)pdm09 infection. We assessed the potential of biomarkers and clinical variables upon admission to the ICU for coinfection diagnosis using CHAID analysis. Performance of cut-off points obtained was determined on the basis of the binominal distributions of the true (+) and true (−) results. Results: Of the 972 patients included, 196 (20.3%) had coinfection. Procalcitonin (PCT; ng/mL 2.4 vs. 0.5, p < 0.001), but not C-reactive protein (CRP; mg/dL 25 vs. 38.5; p = 0.62) was higher in patients with coinfection. In CHAID analyses, PCT was the most important variable for coinfection. PCT <0.29 ng/mL showed high sensitivity (Se = 88.2%), low Sp (33.2%) and high negative predictive value (NPV = 91.9%). The absence of shock improved classification capacity. Thus, for PCT <0.29 ng/mL, the Se was 84%, the Sp 43% and an NPV of 94% with a post-test probability of coinfection of only 6%. Conclusion: PCT has a high negative predictive value (94%) and lower PCT levels seems to be a good tool for excluding coinfection, particularly for patients without shock