Estudio comparativo entre una técnica de reacción en cadena de la polimerasa en transcripción reversa en tiempo real, un método de enzimoinmunoanálisis y el cultivo shell-vial en la detección de virus gripales A y B en pacientes adultos

Reina, Jordi; Plasencia, Virginia; Leyes, María; Nicolau, A.; Galmés, Antònia Pascual; Arbona, Gabriel Mayol

doi:10.1016/j.eimc.2008.11.021

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…Overall, there was a 53% sensitivity difference between RT-PCR and viral isolation in detecting influenza A virus. A sensitivity difference of 9–40% for detection of seasonal influenza A virus has been reported [9], [26]–[29]. We found a slightly wider sensitivity gap of 53% for seasonal influenza A probably because of the difficulty in culturing the circulating A/H3N2 strains.…”

Section: Discussionmentioning

confidence: 47%

Prospective Surveillance and Molecular Characterization of Seasonal Influenza in a University Cohort in Singapore

et al. 2014

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BackgroundSoutheast 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 FindingsWe 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/SignificanceHA 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.

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Section: Discussionmentioning

confidence: 47%

Prospective Surveillance and Molecular Characterization of Seasonal Influenza in a University Cohort in Singapore

et al. 2014

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“…In contrast to CCs, biosurveillance systems that use laboratory-confirmed reports of diseases have higher diagnostic accuracy and lower false alarm rates but take a longer time to detect outbreaks. For example, it takes on average 1.1 days of turnaround time for the results of polymerase chain reaction (PCR)-based laboratory test for influenza to be available after a visit [3]. Most importantly, many patients with influenza may not have a laboratory test ordered due to costs or hospital policies.…”

Section: Introductionmentioning

confidence: 99%

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

Pineda

Visweswaran

et al. 2015

Journal of Biomedical Informatics

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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 randomly 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 1,620 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.

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Comparison of real-time RT-PCR, shell vial culture, and conventional cell culture for the detection of the pandemic influenza A (H1N1) in hospitalized patients

Roa

Catalán

Giannella

et al. 2011

Diagnostic Microbiology and Infectious Disease

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Estudio comparativo entre una técnica de reacción en cadena de la polimerasa en transcripción reversa en tiempo real, un método de enzimoinmunoanálisis y el cultivo shell-vial en la detección de virus gripales A y B en pacientes adultos

Cited by 9 publications

References 21 publications

Prospective Surveillance and Molecular Characterization of Seasonal Influenza in a University Cohort in Singapore

Prospective Surveillance and Molecular Characterization of Seasonal Influenza in a University Cohort in Singapore

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

Comparison of real-time RT-PCR, shell vial culture, and conventional cell culture for the detection of the pandemic influenza A (H1N1) in hospitalized patients

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