The estimated US burden of CAP is substantial, with >1.5 million unique adults being hospitalized annually, 100000 deaths occurring during hospitalization, and approximately 1 of 3 patients hospitalized with CAP dying within 1 year.

The advent of PCR has improved the identification of viruses in patients with communityacquired pneumonia (CAP). Several studies have used PCR to establish the importance of viruses in the aetiology of CAP.We performed a systematic review and meta-analysis of the studies that reported the proportion of viral infection detected via PCR in patients with CAP. We excluded studies with paediatric populations. The primary outcome was the proportion of patients with viral infection. The secondary outcome was short-term mortality.Our review included 31 studies. Most obtained PCR via nasopharyngeal or oropharyngeal swab. The pooled proportion of patients with viral infection was 24.5% (95% CI 21.5-27.5%). In studies that obtained lower respiratory samples in >50% of patients, the proportion was 44.2% (95% CI 35.1-53.3%). The odds of death were higher in patients with dual bacterial and viral infection (OR 2.1, 95% CI 1.32-3.31).Viral infection is present in a high proportion of patients with CAP. The true proportion of viral infection is probably underestimated because of negative test results from nasopharyngeal or oropharyngeal swab PCR. There is increased mortality in patients with dual bacterial and viral infection. @ERSpublications Viral infection is present in a high proportion of patients with community-acquired pneumonia

Our study demonstrated real-world, direct effectiveness of 13-valent pneumococcal conjugate vaccine against vaccine-type community-acquired pneumonia following introduction into a routine immunization program among adults aged ≥65 years, many of whom had immunocompromising and chronic medical conditions.

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Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.

Few patients with community-acquired pneumonia (CAP) require admission to the intensive care unit (ICU-CAP). However, they represent the most severe form of the disease. An understanding of the etiologic agents of ICU-CAP may lead to better treatment decisions and patient outcomes. The objective of this study was to determine the incidence of respiratory viruses in patients with ICU-CAP. This was an observational study conducted in six Kentucky hospitals from December 2008 through October 2011. A case of ICU-CAP was defined as a patient admitted to an ICU with the diagnosis of CAP. The Luminex xTAG multiplex polymerase chain reaction (PCR) assay was used for viral identification. A total of 468 adult and pediatric patients with ICU-CAP were enrolled in the study. A total of 92 adult patients (23 %) and 14 pediatric patients (19 %) had a respiratory virus identified. Influenza was the most common virus identified in adults and the second most common in pediatric patients. This study suggests that respiratory viruses may be common etiologic agents of pneumonia in patients with ICU-CAP. The Centers for Disease Control and Prevention (CDC) recommend empiric anti-influenza therapy during the winter for hospitalized patients with CAP. This study supports this recommendation in patients with ICU-CAP.

The presence of comorbidities is associated with poorer outcomes in CAP. However, when one comorbidity or less was present, we found that being age 80 years or older was a factor that increased mortality. From a clinical standpoint, this study suggests that being age 80 years or older, instead of age 65 years and older, should be considered a risk factor for poor outcome in CAP.

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