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      Commentary On: Performance of the Cobas ® Influenza A/B Assay for Rapid Pcr-Based Detection of Influenza Compared to Prodesse ProFlu+ and Viral Culture : Molecular Technology Poised to Change Testing for Influenza at the Point-of-Care

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          Abstract

          Clinicians are continuously looking for ways to improve the care they deliver, with the goals of optimizing patient outcomes, improving efficiency of health care delivery, and ultimately advancing the health of population. Optimization of clinical care can be particularly challenging for front-line providers (e.g. emergency, urgent care, family and primary care clinicians) who work in high volume, busy episodic care settings, where decisions must be made rapidly, patient throughput is critical, and follow-up is often not possible. One fundamental strategy which can aid in improving patient care is to arm clinicians with reliable diagnostics, which are adapted and customized for both the clinical need, and the setting in which they are intended to be utilized. Broadly speaking, tests that provide the greatest benefit are those with well-defined indications for use, which can provide accurate, reliable, and real-time results (actionable during the patient stay), and can contribute to critical clinical decisions – either therapeutic or disposition-related. Notably, from the front-line physician perspective, current diagnostic tools available for critical infectious disease conditions have lagged significantly behind those that have been advanced for other high impact clinical conditions (e.g. troponin testing for acute cardiac conditions). Although highly advanced high-throughput solutions have been developed for centralized laboratories, lesser focus has been put on the development of point-of-care solutions for bedside or satellite laboratory use. The study by Chen et al., published in this issue, represents another important and welcome advance in the developmental pipeline for infectious disease diagnostics, among front-line clinicians with an important new PCR-based diagnostic tool [1]. Influenza remains an important clinical condition, with regard to burden of disease and morbidity/mortality. Annual global attack rates are estimated at up to 10% for adults and 30% for children [2]. This translates to dramatic seasonal rises in outpatient visits, stressing already overcrowded episodic care sites such as emergency departments [3], producing even greater surges and associated challenges in rendering care during pandemics [4, 5]. In those circumstances, safe and appropriate clinical decision-making for patients with respiratory illnesses (e.g. regarding focused use of anti-virals, antibiotics, and inpatient admission) can be life-saving for some patients. Historically, appropriate treatment and patient disposition has been challenged by relying on either a clinical diagnosis (i.e. influenza-like illness), or traditional antigen based rapid influenza tests (RIDT), each of which suffer from poor to moderate sensitivity [6–7]. The adverse impact of these diagnostic shortfalls is evidenced by several recent emergency department-based studies which demonstrate remarkably low (less than 50%) [8] rates of antiviral treatment for those ultimately confirmed as having influenza, even for those with ‘high risk’ clinical characteristics and/ or co-morbidities, where recommendation to treat are definitive [9]. The early revolutionary innovations in molecular diagnostics have focused on the requirement of the centralized laboratory to provide such new assays. PCR was invented in the early 1980s, and by the late 1980s the first automated thermocyclers became available. While the first FDA-approved PCR-based test was approved in 1991 for Chlamydia trachomatis [10], it was not until the mid-2000s that the first fully automated PCR-instruments were introduced incorporating all steps from sample preparation to amplification and result generation [11, 12]. These developments reflected the move from individual instruments to total laboratory automation, which has now been achieved in centralized laboratories for clinical chemistry and immunoassays [13]. However, these dramatic advancements fall short of guiding front-line clinicians to make rapid decisions about infectious diseases for their patients. More intense recent focus on aligning technical advances with clinical needs has given way to significant new advancement, now opening the door for true practice change. Notable developments in the influenza diagnostic arena, which the report by Chen et al. builds upon, include optimization of assay performance, high sensitivity, speed of 20 minutes, and a small platform footprint. These advances, as well as innovations achieved by other rapid molecular platforms (such as the Cepheid GeneXpert) which permit random access loading and integration of rapid results with the electronic medical record, facilitate real-time resulting to clinicians [14]. The isothermal amplification-based assay (Alere i Influenza A and B) received CLIA waiver status by the FDA this past year, permitting true point of care use with improved turn-around-times (TAT) (15 min) [15]. In this report by Chen et al., another PCR-based molecular assay is introduced that delivers an influenza assay for clinicians combining short TAT (15 min), minimal sample handling (<1 min) as well as a CLIA waiver status. The cobas test cartridge also includes a barcode, permitting future EMR integration, though that component remains to be developed and tested. The methodological advances of the ‘Lab in a tube’ (LIAT) System were achieved through a number of inventions. First, a technology characterized by “flow cycling” uses a flexible reaction vessel and modular sample processors that move the sample to the required temperature [16]. This, together with the low reaction volume and containment of all reagents in the lab-in-a-tube permit the shortened PCR assay time. Several new POC diagnostic technologies for infectious diseases are now being introduced to better align with the clinical need; the next critical step will be to design and conduct studies which systematically address questions of implementation and uptake into the real-world (i.e. doctor’s offices, urgent care settings and emergency departments) where the greatest need exists. Important areas to address include defining exactly which patients will benefit from testing and in what setting, as well as who will perform the test, how quality assurance and quality control activities can be performed and most importantly, the impact of these new generation assays on clinical, health care operations, and the public health, relative to current practice.

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          Most cited references 17

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          Google Flu Trends: correlation with emergency department influenza rates and crowding metrics.

           A. Dugas,  Y-H Hsieh,  S Levin (2012)
           Google Flu Trends (GFT) is a novel Internet-based influenza surveillance system that uses search engine query data to estimate influenza activity and is available in near real time. This study assesses the temporal correlation of city GFT data to cases of influenza and standard crowding indices from an inner-city emergency department (ED).  This study was performed during a 21-month period (from January 2009 through October 2010) at an urban academic hospital with physically and administratively separate adult and pediatric EDs. We collected weekly data from GFT for Baltimore, Maryland; ED Centers for Disease Control and Prevention-reported standardized influenzalike illness (ILI) data; laboratory-confirmed influenza data; and ED crowding indices (patient volume, number of patients who left without being seen, waiting room time, and length of stay for admitted and discharged patients). Pediatric and adult data were analyzed separately using cross-correlation with GFT.  GFT correlated with both number of positive influenza test results (adult ED, r = 0.876; pediatric ED, r = 0.718) and number of ED patients presenting with ILI (adult ED, r = 0.885; pediatric ED, r = 0.652). Pediatric but not adult crowding measures, such as total ED volume (r = 0.649) and leaving without being seen (r = 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r = 0.421) and length of stay for discharged patients (r = 0.548), had moderate correlation with GFT.  City-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool. GFT correlated with several pediatric ED crowding measures and those for low-acuity adult patients.
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            Impact of Seasonal and Pandemic Influenza on Emergency Department Visits, 2003–2010, Ontario, Canada

            Objectives Weekly influenza-like illness (ILI) consultation rates are an integral part of influenza surveillance. However, in most health care settings, only a small proportion of true influenza cases are clinically diagnosed as influenza or ILI. The primary objective of this study was to estimate the number and rate of visits to the emergency department (ED) that are attributable to seasonal and pandemic influenza and to describe the effect of influenza on the ED by age, diagnostic categories, and visit disposition. A secondary objective was to assess the weekly “real-time” time series of ILI ED visits as an indicator of the full burden due to influenza. Methods The authors performed an ecologic analysis of ED records extracted from the National Ambulatory Care Reporting System (NARCS) database for the province of Ontario, Canada, from September 2003 to March 2010 and stratified by diagnostic characteristics (International Classification of Diseases, 10th Revision [ICD-10]), age, and visit disposition. A regression model was used to estimate the seasonal baseline. The weekly number of influenza-attributable ED visits was calculated as the difference between the weekly number of visits predicted by the statistical model and the estimated baseline. Results The estimated rate of ED visits attributable to influenza was elevated during the H1N1/2009 pandemic period at 1,000 per 100,000 (95% confidence interval [CI] = 920 to 1,100) population compared to an average annual rate of 500 per 100,000 (95% CI = 450 to 550) for seasonal influenza. ILI or influenza was clinically diagnosed in one of 2.6 (38%) and one of 14 (7%) of these visits, respectively. While the ILI or clinical influenza diagnosis was the diagnosis most specific to influenza, only 87% and 58% of the clinically diagnosed ILI or influenza visits for pandemic and seasonal influenza, respectively, were likely directly due to an influenza infection. Rates for ILI ED visits were highest for younger age groups, while the likelihood of admission to hospital was highest in older persons. During periods of seasonal influenza activity, there was a significant increase in the number of persons who registered with nonrespiratory complaints, but left without being seen. This effect was more pronounced during the 2009 pandemic. The ratio of influenza-attributed respiratory visits to influenza-attributed ILI visits varied from 2.4:1 for the fall H1N1/2009 wave to 9:1 for the 2003/04 influenza A(H3N2) season and 28:1 for the 2007/08 H1N1 season. Conclusions Influenza appears to have had a much larger effect on ED visits than was captured by clinical diagnoses of influenza or ILI. Throughout the study period, ILI ED visits were strongly associated with excess respiratory complaints. However, the relationship between ILI ED visits and the estimated effect of influenza on ED visits was not consistent enough from year to year to predict the effect of influenza on the ED or downstream in-hospital resource requirements.
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              A rapid and automated sample-to-result HIV load test for near-patient application.

              Current viral load tests for human immunodeficiency virus (HIV) can only be performed in laboratory environments, require highly trained operators and expensive equipment, and have a turnaround time of several hours to days. The Liat HIV Quant Assay proves that such nucleic acid testing can be performed rapidly and easily, allowing application at the point of care. The Liat Analyzer automates the entire assay, including sample preparation, amplification, and detection, in a self-contained and closed-tube system. Dynamic range, limit of detection, and subtype specificity were evaluated. Clinical samples were tested retrospectively to correlate the result of this assay with those of commercial HIV load assays. The Liat assay demonstrated linearity of >6 logs (R(2), 0.98) and a limit of detection of 57 copies/mL. HIV-1 group M (clades A-H), group O, and HIV-2 were detected. Testing of clinical samples showed a high degree of concordance between the copy numbers detected by the Liat assay and those detected by the Siemens and Roche assays (92.0% and 88% correlation coefficient of the log copy number, respectively). The time from sample collection to result was 88 min. Results suggest that the Liat HIV Quant Assay has performance equivalent to that of commercial HIV load assays and significantly reduces assay time, simplifies test operation, and provides biocontainment to allow operation in nonlaboratory settings.
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                Author and article information

                Journal
                Eur J Microbiol Immunol (Bp)
                Eur J Microbiol Immunol (Bp)
                EUJMI
                European Journal of Microbiology & Immunology
                Akadémiai Kiadó (Budapest )
                2062-509X
                2062-8633
                04 December 2015
                December 2015
                : 5
                : 4
                : 233-235
                Affiliations
                [1 ] Department of Emergency Medicine, The Johns Hopkins University , Baltimore MD, USA
                [2 ] Division of Infectious Diseases, Medicine, The Johns Hopkins University , Baltimore MD, USA
                Author notes
                * Department of Emergency Medicine, The Johns Hopkins University, Baltimore MD, USA; rrothma1@ 123456jhmi.edu
                Article
                10.1556/1886.2015.11111
                4681350
                © 2015, The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 16, Pages: 3
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