[Comparison study of a real-time reverse transcription polymerase chain reaction assay with an enzyme immunoassay and shell vial culture for influenza A and B virus detection in adult patients]

Comparison of double-dose vs standard-dose oseltamivir in the treatment of influenza: A systematic review and meta-analysis

WHAT IS KNOWN AND OBJECTIVE

The effect of double-dose oseltamivir on mortality in patients with influenza remains controversial. We systematically reviewed the literature to investigate whether double-dose oseltamivir influences mortality in patients with influenza.

METHODS

PubMed, Excerpta Medica Database (Embase) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for randomized controlled trials (RCTs) and observational studies regarding the effect of double-dose oseltamivir on mortality in patients with influenza. The primary outcome was all-cause mortality. The Mantel-Haenszel method with random effects model was used to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS AND DISCUSSION

Ten studies (four RCTs and six observational studies), involving 20 947 patients, were included. Pooled analysis suggested that double-dose oseltamivir was not associated with decreased mortality in patients with influenza, both in RCTs (OR, 1.29; 95% CI, 0.52-3.15; P = .58) and observational studies (OR, 1.59; 95% CI, 0.79-3.19; P = .20; I²  = 51%). Double-dose oseltamivir did not show statistical benefit on the virologic clearance rate (OR, 1.26; 95% CI, 0.85-1.88; P = .25; I²  = 0%) or the incidence of adverse events (AEs) (OR, 1.52; 95% CI, 0.85-2.72; P = .15; I²  = 59%).

WHAT IS NEW AND CONCLUSION

Current evidence indicates that double-dose oseltamivir does not decrease mortality or have advantages on the outcome of virologic clearance rate and incidence of AEs. However, the finding largely relied on the data from observational studies, which may potentially have led to selection bias. Therefore, high-quality and adequately powered RCTs are warranted.

Clinical Mimics: An Emergency Medicine-Focused Review of Influenza Mimics

BACKGROUND

Influenza viruses are a significant cause of morbidity and mortality in the United States. Given the wide range of symptoms, emergency physicians must maintain a broad differential diagnosis in the evaluation and treatment of patients presenting with influenza-like illnesses.

OBJECTIVE

This review addresses objective and subjective symptoms commonly associated with influenza and discusses important mimics of influenza viruses, while offering a practical approach to their clinical evaluation and treatment.

DISCUSSION

Influenza-like symptoms are common in the emergency department (ED), and influenza accounts for > 200,000 hospitalizations annually. The three predominant types are A, B, and C, and these viruses are commonly transmitted through aerosolized viral particles with a wide range of symptoms. The most reliable means of identifying influenza in the ED is rapid antigen detection, although consideration of local prevalence is required. High-risk populations include children younger than 4 years, adults older than 50 years, adults with immunosuppression or chronic comorbidities, pregnancy, obesity, residents of long-term care facilities, and several others. The Centers for Disease Control and Prevention recommends treatment with neuraminidase inhibitors in these populations. However, up to 70% of patients with these symptoms may have a mimic. These mimics include infectious and noninfectious sources. The emergency physician must be aware of life-threatening mimics and assess for these conditions while beginning resuscitation and treatment.

CONCLUSIONS

The wide range of symptoms associated with influenza overlap with several life-threatening conditions. Emergency physicians must be able to rapidly identify patients at risk for complications and those who require immediate resuscitation.

Comparison of machine learning classifiers for influenza detection from emergency department free-text reports

Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases.

Prospective surveillance and molecular characterization of seasonal influenza in a university cohort in Singapore

BACKGROUND

Southeast Asia is believed to be a potential locus for the emergence of novel influenza strains, and therefore accurate sentinel surveillance in the region is critical. Limited information exists on sentinel surveillance of influenza-like illness (ILI) in young adults in Singapore in a University campus setting. The objective of the present study was to determine the proportion of ILI caused by influenza A and B viruses in a university cohort in Singapore.

METHODOLOGY/PRINCIPAL FINDINGS

We conducted a prospective surveillance study from May through October 2007, at the National University of Singapore (NUS). Basic demographic information and nasopharyngeal swabs were collected from students and staff with ILI. Reverse-transcriptase PCR (RT-PCR) and viral isolation were employed to detect influenza viruses. Sequencing of hemagglutinin (HA) and neuraminidase (NA) genes of some representative isolates was also performed. Overall proportions of influenza A and B virus infections were 47/266 (18%) and 9/266 (3%) respectively. The predominant subtype was A/H3N2 (55%) and the rest were A/H1N1 (45%). The overall sensitivity difference for detection of influenza A viruses using RT-PCR and viral isolation was 53%. Phylogenetic analyses of HA and NA gene sequences of Singapore strains showed identities higher than 98% within both the genes. The strains were more similar to strains included in the WHO vaccine recommendation for the following year (2008). Genetic markers of oseltamivir resistance were not detected in any of the sequenced Singapore isolates.

CONCLUSIONS/SIGNIFICANCE

HA and NA gene sequences of Singapore strains were similar to vaccine strains for the upcoming influenza season. No drug resistance was found. Sentinel surveillance on university campuses should make use of molecular methods to better detect emerging and re-emerging influenza viral threats.