Impact of oseltamivir use on the reduction of complications in patients with influenza: a prospective study

Influenza Vaccination and Guillain-Barré Syndrome: Reality or Fear

Guillain–Barré syndrome (GBS) is an inflammatory disorder and an acute immune-mediated demyelinating neuropathy that causes reduced signal transmissions, progressive muscle weakness, and paralysis. The etiology of the syndrome still remains controversial and uncertain. GBS can be initiated and triggered by respiratory tract infections such as influenza, and intestinal infections such as Campylobacter jejuni. In addition, there is considerable evidence suggesting links between influenza vaccination and GBS. As reported previously, the incidence of GBS in individuals receiving swine flu vaccine was about one to two cases per million. Despite the influenza vaccine efficacy, its association with an immune-mediated demyelinating process can be challenging as millions of people get vaccinated every year. In this review we will discuss the association between influenza infection and vaccination with GBS by focusing on the possible immunopathological mechanisms.

Clinical and laboratory predictors of influenza infection among individuals with influenza-like illness presenting to an urban Thai hospital over a five-year period

Early diagnosis of influenza infection maximizes the effectiveness of antiviral medicines. Here, we assess the ability for clinical characteristics and rapid influenza tests to predict PCR-confirmed influenza infection in a sentinel, cross-sectional study for influenza-like illness (ILI) in Thailand. Participants meeting criteria for acute ILI (fever > 38°C and cough or sore throat) were recruited from inpatient and outpatient departments in Bangkok, Thailand, from 2009-2014. The primary endpoint for the study was the occurrence of virologically-confirmed influenza infection (based upon detection of viral RNA by RT-PCR) among individuals presenting for care with ILI. Nasal and throat swabs were tested by rapid influenza test (QuickVue) and by RT-PCR. Vaccine effectiveness (VE) was calculated using the case test-negative method. Classification and Regression Tree (CART) analysis was used to predict influenza RT-PCR positivity based upon symptoms reported. We enrolled 4572 individuals with ILI; 32.7% had detectable influenza RNA by RT-PCR. Influenza cases were attributable to influenza B (38.6%), A(H1N1)pdm09 (35.1%), and A(H3N2) (26.3%) viruses. VE was highest against influenza A(H1N1)pdm09 virus and among adults. The most important symptoms for predicting influenza PCR-positivity among patients with ILI were cough, runny nose, chills, and body aches. The accuracy of the CART predictive model was 72.8%, with an NPV of 78.1% and a PPV of 59.7%. During epidemic periods, PPV improved to 68.5%. The PPV of the QuickVue assay relative to RT-PCR was 93.0% overall, with peak performance during epidemic periods and in the absence of oseltamivir treatment. Clinical criteria demonstrated poor predictive capability outside of epidemic periods while rapid tests were reasonably accurate and may provide an acceptable alternative to RT-PCR testing in resource-limited areas.

Ensuring the Quality of Point-of-Care Testing in a Large and Decentralized Ambulatory Care Setting

OBJECTIVES

In this project, we assessed the breadth, quality, trends, and outcomes of point-of-care (POC) testing and regulatory compliance in 200 University of California, Los Angeles (UCLA) Health system outpatient clinics.

METHODS

We retrospectively extracted clinic POC test orders, results, and e-prescription data from the UCLA electronic health record over a 1-year period.

RESULTS

Over 100,000 total tests were performed, encompassing 10 POC tests. Initially, 40% of clinics did not have complete licensure, but after implementation of the POC team, this metric improved to 100% licensure within 6 months. Most clinics used two or fewer POC tests, resulted fewer than 200 tests per year, and performed little to no external quality control measures. Our data analytics approach showed that peak POC testing occurred in January 2015, driven by influenza and urinalysis testing, and that both the testing and resulting clinical decision making do not routinely follow society guidelines.

CONCLUSIONS

This decentralization of laboratory testing presents challenges to ensuring quality POC testing. Optimization and analysis of informatics data allow for the identification of POC test utilization trends, areas of improvement for clinical workflows, and increased education on national guidelines.