Impact of non-invasive mechanical ventilation (niv) in critical patients with influenza (H1N1) PDM09

The use of non-invasive mechanical ventilation (NIV) in patients with influenza A (H1N1)pdm09 admitted to intensive care units (ICU) has been controversial

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

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution

Inflammatory response in mixed viral-bacterial community-acquired pneumonia

BACKGROUND: The role of mixed pneumonia (virus + bacteria) in community-acquired pneumonia (CAP) has been described in recent years. However, it is not known whether the systemic inflammatory profile is different compared to monomicrobial CAP. We wanted to investigate this profile of mixed viral-bacterial infection and to compare it to monomicrobial bacterial or viral CAP. METHODS: We measured baseline serum procalcitonin (PCT), C reactive protein (CRP), and white blood cell (WBC) count in 171 patients with CAP with definite etiology admitted to a tertiary hospital: 59 (34.5%) bacterial, 66 (39.%) viral and 46 (27%) mixed (viral-bacterial). RESULTS: Serum PCT levels were higher in mixed and bacterial CAP compared to viral CAP. CRP levels were higher in mixed CAP compared to the other groups. CRP was independently associated with mixed CAP. CRP levels below 26 mg/dL were indicative of an etiology other than mixed in 83% of cases, but the positive predictive value was 45%. PCT levels over 2.10 ng/mL had a positive predictive value for bacterial-involved CAP versus viral CAP of 78%, but the negative predictive value was 48%. CONCLUSIONS: Mixed CAP has a different inflammatory pattern compared to bacterial or viral CAP. High CRP levels may be useful for clinicians to suspect mixed CAP

Effectiveness of combination therapy versus monotherapy with a third-generation cephalosporin in bacteraemic pneumococcal pneumonia: A propensity score analysis

Objective: Combining a macrolide or a fluoroquinolone to beta-lactam regimens in the treatment of patients with moderate to severe community-acquired pneumonia is recommended by the international guidelines. However, the information in patients with bacteraemic pneumococcal pneumonia is limited. Methods: A propensity score technique was used to analyze prospectively collected data from all patients with bacteraemic pneumococcal pneumonia admitted from 2000 to 2015 in our institution, who had received empirical treatment with third-generation cephalosporin in monotherapy or plus macrolide or fluoroquinolone. Results: We included 69 patients in the monotherapy group and 314 in the combination group. After adjustment by PS for receiving monotherapy, 30-day mortality (OR 2.89; 95% CI 1.07-7.84) was significantly higher in monotherapy group. A higher 30-day mortality was observed in monotherapy group in both 1:1 and 1:2 matched samples although it was statistically significant only in 1:2 sample (OR: 3.50 (95% CI 1.03-11.96), P = 0.046). Conclusions: Our study suggests that in bacteraemic pneumococcal pneumonia, empirical therapy with a third-generation cephalosporin plus a macrolide or a fluoroquinolone is associated with a lower mortality rate than beta-lactams in monotherapy. These results support the recommendation of combination therapy in patients requiring admission with moderate to severe disease

Lethal Influenza Virus Infection in Macaques Is Associated with Early Dysregulation of Inflammatory Related Genes

The enormous toll on human life during the 1918–1919 Spanish influenza pandemic is a constant reminder of the potential lethality of influenza viruses. With the declaration by the World Health Organization of a new H1N1 influenza virus pandemic, and with continued human cases of highly pathogenic H5N1 avian influenza virus infection, a better understanding of the host response to highly pathogenic influenza viruses is essential. To this end, we compared pathology and global gene expression profiles in bronchial tissue from macaques infected with either the reconstructed 1918 pandemic virus or the highly pathogenic avian H5N1 virus A/Vietnam/1203/04. Severe pathology was observed in respiratory tissues from 1918 virus-infected animals as early as 12 hours after infection, and pathology steadily increased at later time points. Although tissues from animals infected with A/Vietnam/1203/04 also showed clear signs of pathology early on, less pathology was observed at later time points, and there was evidence of tissue repair. Global transcriptional profiles revealed that specific groups of genes associated with inflammation and cell death were up-regulated in bronchial tissues from animals infected with the 1918 virus but down-regulated in animals infected with A/Vietnam/1203/04. Importantly, the 1918 virus up-regulated key components of the inflammasome, NLRP3 and IL-1β, whereas these genes were down-regulated by A/Vietnam/1203/04 early after infection. TUNEL assays revealed that both viruses elicited an apoptotic response in lungs and bronchi, although the response occurred earlier during 1918 virus infection. Our findings suggest that the severity of disease in 1918 virus-infected macaques is a consequence of the early up-regulation of cell death and inflammatory related genes, in which additive or synergistic effects likely dictate the severity of tissue damage

Impacto cuantitativo de la contaminación en la probabilidad de muerte por neumonía por SARS-CoV-2

Introducción La evidencia científica disponible señala que la contaminación del aire exterior podría agravar la severidad de la COVID-19 y por ende, incrementar las probabilidades de fallecimiento. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales: 2 en Bizkaia (1 urbano, 1 urbano-rural), Valencia y Barcelona (urbanos). Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Para determinar la exposición a contaminación por PM$_{10}$ y NO$_{2}$, se obtuvieron los datos publicados por los organismos autonómicos de calidad del aire, para 2019 y 1er semestre 2020. Se utilizó un Modelo Aditivo Generalizado (GAM) para estimar el nivel diario de contaminante en cada código postal, en función de las coordenadas geográficas y la altitud de las estaciones de medición [Figura 1]. Para determinar la exposición crónica, se calcularon media y máximo en 2019; la aguda se caracterizó por media y máximo en los 7 días anteriores al ingreso. Se estudió la razón de probabilidades (‘odds ratio’, OR) de muerte frente a supervivencia entre nuestra cohorte. Se modeló mediante un GAM con regresión logística, incorporando como efectos fijos sexo, edad y contaminante; hospital como efecto aleatorio e índice de comorbilidad de Charlson como función suave mediantes splines penalizados. Resultados De los 1548 pacientes reclutados, 243 (15.7%) fallecieron durante su hospitalización y/o 30 días postingreso. Según los modelos [Tabla 1], existe evidencia estadística significativa de que la exposición crónica a PM$_{10}$ y NO$_{2}$ incrementan la probabilidad de muerte por neumonía SARS-CoV-2. Compensando por sexo, edad y Charlson -todos factores relacionados positivamente con el OR de muerte- así como por hospital; por cada incremento de 10 μg/m$^{3}$ en el nivel de PM$_{10}$ (máximo anual) el OR aumenta en 10.5%, linealmente proporcional al incremento en la contaminación. Mientras, cada 10 μg/m$^{3}$ más de NO2 (media anual) aumentan OR en 35.7%; cada 10 μg/m$^{3}$ más en exposición aguda a NO2 (media semana pre-ingreso): 62.9%; y NO$_{2}$ (máximo semana): 34.4%. Conclusiones Se cuantificaron y compensaron los efectos de los factores sexo, edad, Charlson y hospital. A igualdad de estos, incrementos en la exposición crónica y aguda a PM$_{10}$ y NO$_{2}$ aumentan de manera lineal y estadísticamente significativa la probabilidad de muerte por neumonía SARS-CoV-2

Predicción de la gravedad de neumonías por SARS-CoV-2 a partir de información clínica y contaminación, mediante inteligencia artificial

Introducción La contaminación del aire exterior se ha relacionado con mayor gravedad de las infecciones respiratorias. Por tanto, su inclusión en algoritmos predictivos podrían añadir información para pronosticar la gravedad de neumonías SARS-CoV-2. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales. Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Se recogieron hasta 93 variables clínicas, analíticas y radiológicas por cada paciente (sexo, edad, peso, comorbilidades, síntomas, variables fisiológicas en urgencias, sangre, gasometría, etc.). Además, se calcularon los niveles exposición a contaminación por PM$_{10}$, PM$_{2.5}$, O$_{3}$, NO$_{2}$, NO, NO$_{X}$, SO$_{2}$ y CO en su código postal. En función de la evolución clínica de la neumonía, se definieron 3 niveles de gravedad [Tabla 1]. Para predecir dicha gravedad, se desarrolló un algoritmo de inteligencia artificial (IA), tipo ‘Random Forest’ con balanceo y ajuste automático de sus parámetros internos. El algoritmo se entrenó y evaluó mediante 20 repeticiones de validación cruzada 10-fold (90% entrenamiento, 10% validación), estratificando aleatoriamente por hospital y gravedad. Resultados En los conjuntos de validación, el algoritmo alcanzó una capacidad predictiva (área bajo la curva ROC) promedio AUC=0.834 para gravedad nivel 0, AUC=0.724 para 1 y AUC=0.850 para 2 [Figura 1]. Sin la información de contaminantes, su capacidad predictiva se degradó ligeramente (AUCs = 0.829, 0.722, 0.844; respectivamente). Conclusiones Nuestro algoritmo IA es capaz de predecir de manera satisfactoria la evolución de la gravedad en la neumonía; en particular para los casos más leves y más severos. El algoritmo IA extrae las reglas más relevantes a partir principalmente de la información clínica, analítica y radiológica de cada individuo; no obstante, la incorporación de la exposición a contaminantes mejora ligeramente la capacidad predictiva. El impacto de la contaminación podría estar ya reflejado en las analíticas de sangre, a través de su efecto en los niveles de inflamación del paciente (PCT, PCR, LDH, etc.)