Diagnostic accuracy of a rapid RT-PCR assay for point-of-care detection of influenza A/B virus at emergency department admission: A prospective evaluation during the 2017/2018 influenza season

Maignan, Maxime; Viglino, Damien; Hablot, Maud; Masson, Nicolas Termoz; Lebeugle, Anne; Muret, Roselyne Collomb; Makele, Prudence Mabiala; Guglielmetti, Valérie; Morand, Patrice; Lupo, Julien; Forget, Virginie; Landelle, Caroline; Larrat, Sylvie

doi:10.1371/journal.pone.0216308

Cited by 39 publications

(28 citation statements)

References 39 publications

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“…FTA cards can store blood samples for up to 22 years and buccal samples for up to 12 years at room temperature. However, these commercial papers have not be fully used for the sample in-result out bedside diagnosis, because they still need combine with the downstream analysis technologies (e.g., PCR [12], RT-PCR [121]) to accomplish the testing step.…”

Section: Sample Storage and Collectionmentioning

confidence: 99%

A review on advances in methods for modification of paper supports for use in point-of-care testing

et al. 2019

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Paper is a widely used support for use in devices for point-of-care testing (POCT) in clinical diagnosis, food safety monitoring and environmental pollution. Paper is inexpensive, biocompatible, biodegradable and allows a sample fluid to flow by capillary force. Numerous method have been developed recently for chemical modification of papers in order to introduce different functionalities. This review (with 148 refs.) summarizes the recent progress in paper-based POCT devices. The introduction summarizes the state of the art of paper-based POCT devices and the physicochemical properties of existing unmodified materials (including cellulose, cellulose-based composites, cotton fibers, glass fibers, nitrocellulose, thread). Methods for paper modification for sample pretreatment are summarized next, with subsections on sample storage and collection, sample separation, nucleic acid extraction and sample preconcentration. Another main section covers approaches for paper modification for improving POCTs, with subsections on assays for proteins, nucleic acids, drugs, ion and organic molecules. The advantages and disadvantages of these approaches are compared. Several tables are presented that summarize the various modification techniques. A concluding section summarizes the current status, addresses challenges and gives an outlook on future perspectives of POCTs.

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Section: Sample Storage and Collectionmentioning

confidence: 99%

A review on advances in methods for modification of paper supports for use in point-of-care testing

et al. 2019

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“…The high sensitivity and specificity for influenza A, influenza B and RSV found in our study was comparable to previous findings, with better diagnostic accuracy than other routinely available point-of-care devices. [14][15][16][17][18][20][21][22][23][24][25][26][27][28][29][30] The diagnostic accuracy of POCTs for influenza has been examined in individual studies and several systematic reviews. [31][32][33] Although the impact of the cobas® Liat® POCT on clinical decision making during visits to the emergency department has been established, [34] the effect on patient-relevant clinical outcomes still has to be determined.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…An rCt of Clinical and Cost effectiveness in primary CarE (ALIC 4 E)" trial, [12,13] to evaluate the cobas® Liat® POCT, selected as it is easy to use and has excellent proof-of-principle supporting data. [14][15][16][17][18] However, before uptake into routine care, it is critical that analytic performance be established using samples obtained from real patients in the setting in which the test might be marketed.…”

mentioning

confidence: 99%

Performance and ease of use of a molecular point-of-care test for influenza A/B and RSV in patients presenting to primary care

Verbakel

Matheeussen

Loens

et al. 2020

Eur J Clin Microbiol Infect Dis

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Annual influenza epidemics cause substantial morbidity and mortality, and the majority of patients with influenza-like illness present to primary care. Point-of-care influenza tests could support treatment decisions. It is critical to establish analytic performance of these platforms in real-life patient samples before uptake can be considered. We aimed to assess the analytical performance and ease of use of the cobas® Liat® PCR POCT in detecting influenza A/B and RSV in samples collected from patients with influenza-like illness in primary care. Sensitivity and specificity of the cobas® Liat® POCT are calculated in comparison with a commercial laboratory-based PCR test (Fast-Track Respiratory Pathogens 21 Plus kit (Fast-Track Diagnostics)). Samples with discordant results were analysed additionally by the RespiFinder 2Smart (PathoFinder) using an Extended Gold Standard (EGS). Acceptability was scored on a five-point Likert scale as well as a failure mode analysis of the cobas® Liat® POCT was performed. Nasal and oropharyngeal swabs were obtained from 140 children and nasopharyngeal swabs from 604 adults (744 patients). The cobas® Liat® POCT had a sensitivity and specificity of 100% (95% CI 99-100%) and 98.1% (95%CI 96.3-99%) for influenza A, 100% (95% CI 97.7-100%) and 99.7% (95%CI 98.7-99.9%) for influenza B and 100% (95% CI 87.1-100%) and 99.4% (95%CI 98.6-99.8%) for RSV, respectively. According to trained lab technicians, the cobas® Liat® POCT was considered easy-to-use, with a fast turnaround time. Cobas® Liat® POCT is a promising decentralised test platform for influenza A/B and RSV in primary care as it provides fairly rapid results with excellent analytic performance. Point-of-care influenza tests could support treatment decisions in primary care. Cobas® Liat® POCT is a promising decentralised test platform for influenza A/B and RSV in primary care as it provides fairly rapid results with excellent analytic performance. Keywords Influenza. Point-of-care. Primary care. Ease-of-use. PCR BACKGROUND Annual influenza epidemics cause substantial morbidity and mortality and the 2009 H1N1 influenza outbreak was defined as a pandemic by the World Health Organisation (WHO). [1, 2] About 50% of patients presenting to primary care with influenza-like illness (ILI) during a period of high influenza incidence, with some variation by country, usually have laboratory-confirmed influenza infection. [3] Unlike in Japan for example, [4] in European primary care, point-of-care testing for influenza is not routine practice, making it difficult for clinicians to distinguish influenza from other viral infections that cause ILI at the point of care with a high degree of certainty. [5] It is common practice throughout Europe to base treatment or advice to patients with ILI on signs and symptoms alone, given that point-of-care tests (POCTs) may neither be feasible nor perform adequately, and that antiviral Electronic supplementary material The online version of this article (

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“…While the rapid RT-PCR assay is a very robust technique for diagnosis of In uenza A and B with a sensitivity of 98% and speci city of 99% (11), the same thing is not true for COVID-19 PCR. The accuracy of PCR in COVID-19 depends on the time and technique of sampling.…”

Section: Reverse Transcription-polymerase Chain Reaction (Rt-pcr)mentioning

confidence: 99%

The feasibility of transfer learning for differentiation H1N1 Influenza from COVID-19 on chest CT

Sotoudeh¹,

Tasorian²,

Tabatabaei³

et al. 2020

Preprint

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Objectives: It is unlikely that by fall and winter of 2020, standard vaccine or treatment is available for COVID-19 infection. In this period, differentiation between COVID-19 and Influenza induced pneumonia will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall and F1 score of each model were calculated.Results: For differentiation of COVID-19 pneumonia versus H1N1 Influenza pneumonia versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.

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Diagnostic accuracy of a rapid RT-PCR assay for point-of-care detection of influenza A/B virus at emergency department admission: A prospective evaluation during the 2017/2018 influenza season

Cited by 39 publications

References 39 publications

A review on advances in methods for modification of paper supports for use in point-of-care testing

A review on advances in methods for modification of paper supports for use in point-of-care testing

Performance and ease of use of a molecular point-of-care test for influenza A/B and RSV in patients presenting to primary care

The feasibility of transfer learning for differentiation H1N1 Influenza from COVID-19 on chest CT

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