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Abstract Objective Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results Among 8032 patients with laboratory‐confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86–0.93) for prediction of critical care and 0.90 (95% CI 0.88–0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision‐making.

Abstract Several vaccines have been introduced to combat the coronavirus infectious disease‐2019 (COVID‐19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Current SARS‐CoV‐2 vaccines include mRNA‐containing lipid nanoparticles or adenoviral vectors that encode the SARS‐CoV‐2 Spike (S) protein of SARS‐CoV‐2, inactivated virus, or protein subunits. Despite growing success in worldwide vaccination efforts, additional capabilities may be needed in the future to address issues such as stability and storage requirements, need for vaccine boosters, desirability of different routes of administration, and emergence of SARS‐CoV‐2 variants such as the Delta variant. Here, we present a novel, well‐characterized SARS‐CoV‐2 vaccine candidate based on extracellular vesicles (EVs) of Salmonella typhimurium that are decorated with the mammalian cell culture‐derived Spike receptor‐binding domain (RBD). RBD‐conjugated outer membrane vesicles (RBD‐OMVs) were used to immunize the golden Syrian hamster (Mesocricetus auratus) model of COVID‐19. Intranasal immunization resulted in high titres of blood anti‐RBD IgG as well as detectable mucosal responses. Neutralizing antibody activity against wild‐type and Delta variants was evident in all vaccinated subjects. Upon challenge with live virus, hamsters immunized with RBD‐OMV, but not animals immunized with unconjugated OMVs or a vehicle control, avoided body mass loss, had lower virus titres in bronchoalveolar lavage fluid, and experienced less severe lung pathology. Our results emphasize the value and versatility of OMV‐based vaccine approaches.

Influenzavirus is among the most relevant candidates for a next pandemic. We review here the phylogeny of former influenza pandemics, and discuss candidate lineages. After briefly reviewing the other existing antiviral options, we discuss in detail the evidences supporting the efficacy of passive immunotherapies against influenzavirus, with a focus on convalescent plasma., (© 2024 John Wiley & Sons Ltd.)

Background: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study., Methods: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza., Results: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups., Conclusions: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection., (© 2022 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Background: Influenza B accounts for approximately one fourth of the seasonal influenza burden. However, research on the importance of influenza B has received less attention compared to influenza A. We sought to describe the association of both coinfections and comorbidities with disease severity among adults presenting to emergency departments (ED) with influenza B., Methods: Nasopharyngeal samples from patients found to be influenza B positive in four US and three Taiwanese ED over four consecutive influenza seasons (2014-2018) were tested for coinfections with the ePlex RP RUO panel. Multivariable logistic regressions were fitted to model adjusted odds ratios (aOR) for two severity outcomes separately: hospitalization and pneumonia diagnosis. Adjusting for demographic factors, underlying health conditions, and the National Early Warning Score (NEWS), we estimated the association of upper respiratory coinfections and comorbidity with disease severity (including hospitalization or pneumonia)., Results: Amongst all influenza B positive individuals (n = 446), presence of another upper respiratory pathogen was associated with an increased likelihood of hospitalization (aOR = 2.99 [95% confidence interval (95% CI): 1.14-7.85, p = 0.026]) and pneumonia (aOR = 2.27 [95% CI: 1.25-4.09, p = 0.007]). Chronic lung diseases (CLD) were the strongest predictor for hospitalization (aOR = 3.43 [95% CI: 2.98-3.95, p < 0.001]), but not for pneumonia (aOR = 1.73 [95% CI: 0.80-3.78, p = 0.166])., Conclusion: Amongst ED patients infected with influenza B, the presence of other upper respiratory pathogens was independently associated with both hospitalization and pneumonia; presence of CLD was also associated with hospitalization. These findings may be informative for ED clinician's in managing patients infected with influenza B., (© 2021 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)