False-positive COVID-19 results: hidden problems and costs

Суркова, Елена; Nikolayevskyy, Vladyslav; Drobniewski, Francis

doi:10.1016/s2213-2600(20)30453-7

Cited by 314 publications

(357 citation statements)

References 2 publications

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“…This precludes estimation of specificity and results in the sampling method with the most positive results being the most sensitive method. Contamination is the most likely source of false positives ( 76 ). However, we did not observe systematic trends across included studies, which may suggest that contamination with 1 sampling method was driving results.…”

Section: Discussionmentioning

confidence: 99%

The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs

et al. 2021

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Section: Discussionmentioning

confidence: 99%

The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs

et al. 2021

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“… 16 – 18 Even a very small false-positive rate ( e.g., <1%) may result in a significant proportion of false-positive results, especially when the prevalence of the virus in the population is low. 19 One unique feature of our QUIT SARS-CoV-2 is that it only isolates and analyses intact virus particles while getting rid of most viral debris, which otherwise can still be detected from a large proportion of recovered patients. 20 We prepared two aliquots of viral specimens with one of them lysed before the study.…”

mentioning

confidence: 99%

Quantitative and Ultrasensitive In-situ Immunoassay Technology for SARS-CoV-2 Detection in Saliva

Lee

Liu

Chen

et al. 2021

Preprint

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The COVID-19 pandemic has become an immense global health crisis. However, the lack of efficient and sensitive on-site testing methods limits early detection for timely isolation and intervention. Here, we present a Quantitative and Ultrasensitive in-situ Immunoassay Technology for SARS-CoV-2 detection in saliva (QUIT SARS-CoV-2). Our nanoporous membrane resonator generates a rapid oscillating flow to purify and concentrate SARS-CoV-2 virus in saliva by 40 folds for in-situ detection of viral antigens based on chemiluminescent immunoassay within 20 min. This method achieved a detection sensitivity below 10 0 copies/mL viral load, comparable to the bench-top PCR equipment. The portable QUIT SARS-CoV-2 system, allowing rapid and accurate on-site viral screen with high-throughput sample pooling strategy, can be performed at the primary care settings and substantially improve the detection and prevention of COVID-19.

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“…It must be pointed out that a mean of five chest CT scans was performed on each patient which brought radiation exposure issue. But under the actual pandemic pressure in that period in Wuhan, China, the shortage and high false-negative rates of the RT-PCR tests (about 2–33%) made clinical doctors chose chest CT scans as the first modality in the screening or follow-up for suspicious or confirmed COVID-19 patients which was cheaper and faster in China 4 , 5 , 13 , 34 . However, after the improvement of the shortage of RT-PCR tests in China, the chest CT scan was not firstly recommended at present.…”

Section: Discussionmentioning

confidence: 99%

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

Pan

Liu

et al. 2021

Sci Rep

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This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

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False-positive COVID-19 results: hidden problems and costs

Cited by 314 publications

References 2 publications

The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs

The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs

Quantitative and Ultrasensitive In-situ Immunoassay Technology for SARS-CoV-2 Detection in Saliva

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

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