Four scientists: the corona test is unreliable and the testing policy clearly fails

 

The current corona testing policy is failing. By testing less and better, unnecessary damage to welfare and the economy would be prevented, write Dr Carla Peeters, Prof. Wim Vanden Berghe and Prof. Dr Mattias Desmet in this article. Prof. Dr. Robert Gorter added his commentaries

Worldwide, under pressure from the World Health Organization (WHO), politicians, virologists and epidemiologists, the Corona testing policy (later in this article: the RT-qPCR testing policy) is being greatly expanded to improve the mapping of infections with the new coronavirus (later in this article: nCoV2019). In a short time, the number of tests rose from thousands to hundreds of thousands per week. As a result, the test system is under high pressure with rapidly increasing problems such as shortages of skilled personnel and lack of available materials, expensive machines and laboratory spaces. As a result, waiting times for testing increase and it often takes a long time for test results to be available.

In addition, the RT-qPCR test used, which is regarded worldwide as the gold standard for detecting the nCoV2019, is not without validity problems. For example, the test does not differentiate between a piece of RNA (ribonucleic acid) that comes from an old infection and a virus that is capable of causing an infection. Without additional diagnostics, there is a risk that people will unfairly experience the stress of being infected while they are not at all, will be quarantined and / or other unnecessary measures will be taken that will harm well-being and the economy. There is an urgent need for refinement and standardization of the testing and analysis policy, with a differentiated strategy for different risk groups.

How is it tested?

Tests based on the gene sequence of the SARS-COV-2 virus announced in January have been put on the market by various parties [1, 2]. The most commonly used test is the RT-qPCR molecular test, which checks whether a portion of the RNA of the E and RdRp genes of coronavirus coat proteins are present in a body [1, 2].

The molecular RT-qPCR assay was designed in a very short time at the start of the pandemic by scientists from the public and academic sectors [3]. The process of evaluating studies for in vitro diagnostic qualifications, which normally takes months, was completed in one week through an efficient collaboration.

At the start of the pandemic, the test was exclusively used by doctors to determine which viral or bacterial infection is the cause of (frequently) common complaints such as fever, cough, shortness of breath, pneumonia and Acute Respiratory Distress Syndrome (ARDS). This is necessary because these complaints can also be caused by other viruses or bacteria. In addition to the RT-qPCR test, therefore, a CT scan was also performed in many cases. For routine analysis, the E gene analysis is recommended, followed by the RdRp gene analysis which can differentiate the nCoV2019 RNA from the SARS COV RNA with two probes and dual coloring.

With the current test, there is a risk that many people unfairly experience the stress of being infected while they are not infected at all, and will be quarantined; and the psychological and economic consequences of two weeks of quarantine are often enormous.

From June 2020, the use of the RT-qPCR test was extended to people of all ages with less severe symptoms (cough, sore throat, headache, fever, shortness of breath) or even without symptoms at all (e.g. contacts, people who have traveled and healthcare and education personnel who want to undergo preventive testing). From then on, the test was often used by a doctor without any additional clinical diagnosis. This practice is highly debatable for several reasons.

Both Centers for Disease Control and Prevention (CDC), U.S. Food and Drug Administration (FDA) and the test package insert (Roche Tib-Molbiol) write that detecting viral RNA from nCoV2019 with the RT-qPCR test does not mean the virus is also the cause of the possible clinical symptoms [4–6]. In other words: the test can by no means rule out that other viruses or bacteria can (partly) be the cause of the disease symptoms.

For example, in the last eleven weeks, despite a sharp increase in the number of positive tests of nCoV2019 measured in the national test streets, the rhinovirus was mainly detected in the samples collected by general practitioners from persons with acute respiratory infections and analyzed in the reference laboratories [ 7].

In addition, the RT-qPCR test detects RNA from a virus particle, but this may be from an old, cured infection and not always from an intact ‘virulent’ virus that is contagious and may cause a new infection. cause. The transmission risk and the reproduction value are therefore difficult to determine by means of an RT-qPCR test [8, 9].

Test results are approx. 48% unreliable

What is often forgotten is that in addition to the presence of the COVID-19 virus in the tested person, various other factors also have a (major) influence on the outcome of the test. A positive result of the RT-qPCR test is determined by the number of cycles it takes to obtain a measurable signal. The cycle value (Ct value) not only depends on the amount of RNA or the number of virus particles in the sample, but also, for example, on the moment of sample collection in the course of the infection, the way in which the sample is taken (nose or throat) and the time that elapses before the sample is analyzed [2, 10].

Chinese research shows that the outcome of the RT-qPCR test can change from positive to negative and then back to positive over the course of COVID-19 infection cycle [11]. On the other hand, it appears that a large number of people continue to take a positive RT-qPCR test (weeks to months) after the clinical symptoms have long disappeared and they are no longer infectious to others [12-14].

Differences in interpretation and assessment between shortly trained and experienced laboratory personnel and the risk of administrative errors should not be underestimated under an enormous workload [15]. In a reference manual for PCR testing, it is recommended to read positive only when the Ct value is below 24. Values ​​between 24 – 35 are seen as a gray area [9]. At higher Ct values, the test is defined as less reliable.

In current testing policy, Ct values ​​between 35 and 45 are sometimes reported, which should actually be considered unreliable [16]. Remarkably, high numbers of nCoV2019 RNA particles were observed in both asymptomatic and severely ill COVID-19 patients, making it difficult to predict health risks based on a positive RT-qPCR test alone [17]. In addition, virus particle (viral load) concentrations between two RT-qPCR-positively diagnosed samples may differ more than 1 million times. Nevertheless, these samples are considered statistically equivalent, as reporting is done solely on the basis of qualitative (positive or negative) classification and not on the basis of more nuanced quantitative Ct values ​​[9]. The results for the RT-qPCR test do not explain how a positive or negative rating was achieved; positive or negative for the different probes on the E gene and RdRp gene.

The corona test can by no means rule out that other viruses or bacteria can (partly) be the cause of the disease symptoms of the tested person

Under ideal conditions, the data from the RT-qPCR test suggests a high specificity (98-100%). Specificity refers to the test’s ability to correctly detect negative cases. Sensitivity refers to the ability to correctly detect positive cases; this varies between 63 and 78%. Because laboratories use different testing and extraction methods and not all laboratories report Ct values, only the result (positive or negative) is communicated to the government and the patient. In the light of the above problems it is clear that this is very problematic and that it is better to aim for a general algorithm that can be used by different laboratories for a more nuanced quantitative interpretation [18].

In addition, there is an urgent need for loop studies where the same samples are tested by multiple laboratories for quality control (sensitivity, specificity, false positive, false negative, universal standard) or to compare the reliability of different tests. [3, 18-20]. In Germany this was carried out months ago by the government (Robert Koch Institute in Berlin) and it turned out that false positive results were found in 48% of the cases.

High reliability of tests is crucial for correct diagnosis and appropriate measures

For example, in the last eleven weeks, despite a sharp increase in the number of positive tests of nCoV2019 measured in the national test streets, the rhinovirus was mainly detected in the samples collected by general practitioners from persons with acute respiratory infections and analyzed in the reference laboratories [ 7].

In addition, the RT-qPCR test detects RNA from a virus particle, but this may be from an old, cured infection and not always from an intact ‘virulent’ virus that is contagious and may cause a new infection. cause. The transmission risk and the reproduction value are therefore difficult to determine by means of an RT-qPCR test [8, 9].

Test results are approx. 48% unreliable

What is often forgotten is that in addition to the presence of the COVID-19 virus in the tested person, various other factors also have a (major) influence on the outcome of the test. A positive result of the RT-qPCR test is determined by the number of cycles it takes to obtain a measurable signal. The cycle value (Ct value) not only depends on the amount of RNA or the number of virus particles in the sample, but also, for example, on the moment of sample collection in the course of the infection, the way in which the sample is taken (nose or throat) and the time that elapses before the sample is analyzed [2, 10].

Chinese research shows that the outcome of the RT-qPCR test can change from positive to negative and then back to positive over the course of COVID-19 infection cycle [11]. On the other hand, it appears that a large number of people continue to take a positive RT-qPCR test (weeks to months) after the clinical symptoms have long disappeared and they are no longer infectious to others [12-14].

Differences in interpretation and assessment between shortly trained and experienced laboratory personnel and the risk of administrative errors should not be underestimated under an enormous workload [15]. In a reference manual for PCR testing, it is recommended to read positive only when the Ct value is below 24. Values ​​between 24 – 35 are seen as a gray area [9]. At higher Ct values, the test is defined as less reliable.

In current testing policy, Ct values ​​between 35 and 45 are sometimes reported, which should actually be considered unreliable [16]. Remarkably, high numbers of nCoV2019 RNA particles were observed in both asymptomatic and severely ill COVID-19 patients, making it difficult to predict health risks based on a positive RT-qPCR test alone [17]. In addition, virus particle (viral load) concentrations between two RT-qPCR-positively diagnosed samples may differ more than 1 million times. Nevertheless, these samples are considered statistically equivalent, as reporting is done solely on the basis of qualitative (positive or negative) classification and not on the basis of more nuanced quantitative Ct values ​​[9]. The results for the RT-qPCR test do not explain how a positive or negative rating was achieved; positive or negative for the different probes on the E gene and RdRp gene.

The corona test can by no means rule out that other viruses or bacteria can (partly) be the cause of the disease symptoms of the tested person

Under ideal conditions, the data from the RT-qPCR test suggests a high specificity (98-100%). Specificity refers to the test’s ability to correctly detect negative cases. Sensitivity refers to the ability to correctly detect positive cases; this varies between 63 and 78%. Because laboratories use different testing and extraction methods and not all laboratories report Ct values, only the result (positive or negative) is communicated to the government and the patient. In the light of the above problems it is clear that this is very problematic and that it is better to aim for a general algorithm that can be used by different laboratories for a more nuanced quantitative interpretation [18].

In addition, there is an urgent need for loop studies where the same samples are tested by multiple laboratories for quality control (sensitivity, specificity, false positive, false negative, universal standard) or to compare the reliability of different tests. [3, 18-20]. In Germany this was carried out months ago by the government (Robert Koch Institute in Berlin) and it turned out that false positive results were found in 48% of the cases.

High reliability of testing is crucial for correct diagnosis and appropriate measures

Increasing calls from WHO and compliant governments for further testing and use of foreign laboratories, pooled samples, or sewage testing to detect regional outbreaks also require continued attention to the quality of the testing procedure. Recently, a solution is also being sought for the capacity problem by using new commercial rapid tests, which are developed under great time pressure. However, most of these so-called “point of care tests” are based on antigen detection and can only detect high concentrations of the coat protein. The rapid tests are less labor intensive and take less time to produce a result (approximately 30 minutes instead of several hours or more for an RT-qPCR test). However, they are much less sensitive than the RT-qPCR test [27]. The reliability and feasibility of the new point of care tests are currently being compared with the results from RT-qPCR and serological tests, which, however, are themselves insufficiently validated.

To increase the quality of tests and to be able to interpret test results more reliably, it is better to only use tests for people with disease symptoms. The frequent testing of people with mild or asymptomatic complaints in areas where nCoV2019 is already common and has no added value in terms of treatment or prevention. There is no specific treatment for these people and therefore the test cannot make a difference in this area. If tests are no longer used for this target group, the released testing capacity can be used for sick patients and healthcare personnel [16], for example to perform an additional CT scan, serological, immunological and nutritional examination in patients with moderate to severe flu symptoms. This allows the nCoV2019 infection rate and serious COVID-19 disease risks to be estimated more reliably and used more responsibly for more selective mandatory quarantine measures [28-30].

A new flu season requires reflection on current policy

With the colder temperatures on the Northern Hemisphere, we are on the eve of a new flu season and the risk of colds and flu symptoms from all kinds of viruses, such as the influenza virus, rhino & adeno viruses and the corona virus, is increasing. Identifying nCoV2019 contamination solely on the basis of an RT-qPCR test becomes even more difficult this season. This shows us even more the importance of a valid and reliable testing and diagnosis procedure, which allows differentiating between different types of infections and their infectivity to others.

One has to wonder why politicians in many countries are taking draconian measures with incalculable damage to the entire population and against the advice of a rapidly growing number of scientists, who warn of the unreliability of the PCR tests and warn of the consequences for all of society. It seems that the politicians are blindly following the pressure from the WHO. And the WHO and the US CDC roll from one corruption scandal to another … with Bill Gates as one of the WHO’s biggest donors in the background: coincidence?

The interviews were summarized into an article by Dr Carla Peeters.

 

 

 

Over de auteurs

Dr. ir. Carla Peeters. Immunoloog, werkte jaren op het RIVM en was bestuurder van zorgorganisaties. COBALA Good Care Feels Better ®.

Prof. dr. Wim Vanden Berghe. Faculteit Biomedische Wetenschap, PPES Labo Eiwitchemie, Proteoomanalyse & Epigenetische signalering, Universiteit Antwerpen.

Prof. dr. Mattias Desmet. Faculteit Psychologie en Onderwijs Wetenschappen, Universiteit Gent.

Prof. dr. Robert Gorter (emeritus professor University of California San Francisco Medical School (UCSF) schreef aanvullende commentaren.

 

Referenties

1. Kilic, T., R. Weissleder, and H. Lee, Molecular and Immunological Diagnostic Tests of COVID-19: Current Status and Challenges. iScience, 2020. 23(8): p. 101406.
2. Yan, Y., L. Chang, and L. Wang, Laboratory testing of SARS-CoV, MERS-CoV, and SARS-CoV-2 (2019-nCoV): Current status, challenges, and countermeasures. Rev Med Virol, 2020. 30(3): p. e2106.
3. Corman, V.M., et al., Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill, 2020. 25(3).
4. Roche, https://pim-eservices.roche.com/eLD/web/pi/en/home. 2020.
5. CDC, https://www.fda.gov/media/134922/download, 2020.
6. FDA, https://www.fda.gov/media/136151/download, 2020.
7. RIVM, https://www.rivm.nl/griep-griepprik/feiten-en-cijfers, 2020.
8. Rao, S.N., et al., A Narrative Systematic Review of the Clinical Utility of Cycle Threshold Values in the Context of COVID-19. Infect Dis Ther, 2020. 9(3): p. 573-586.
9. Jefferson, T., et al., Viral cultures for COVID-19 infectivity assessment. Systematic review. medRxiv, 2020. https://doi.org/10.1101/2020.08.04.20167932.
10. Haddar, C., et al., Brief comparative evaluation of six open one-step RT-qPCR mastermixes for the detection of SARS-CoV-2 RNA using a Taqman probe. Journal of Clinical Virology, 2020. in press: p. https://doi.org/10.1016/j.jcv.2020.104636.
11. Xiao, A.T., et al., Dynamic profile of RT-PCR findings from 301 COVID-19 patients in Wuhan, China: A descriptive study. J Clin Virol, 2020. 127: p. 104346.
12. Gombar, S., et al., Persistent detection of SARS-CoV-2 RNA in patients and healthcare workers with COVID-19. J Clin Virol, 2020. 129: p. 104477.
13. Kang, H., et al., Retest positive for SARS-CoV-2 RNA of “recovered” patients with COVID-19: Persistence, sampling issues, or re-infection? J Med Virol, 2020.
14. Walsh, K.A., et al., SARS-CoV-2 detection, viral load and infectivity over the course of an infection. J Infect, 2020. 81(3): p. 357-371.
15. G., G., Testing Times. . Nature, 2020. 583: p. 506-509.
16. Zitek, T., The Appropriate Use of Testing for COVID-19. West J Emerg Med, 2020. 21(3): p. 470-472.
17. Yonker, L.M., et al., Pediatric SARS-CoV-2: Clinical Presentation, Infectivity, and Immune Responses. J Pediatr, 2020.
18. Sciensano, COVID19 factsheet 14 juni 2020. https://covid-19.sciensano.be/sites/default/files/Covid19/COVID-19_fact_sheet_ENG.pdf, 2020.
19. LeBlanc, J.J., et al., Real-time PCR-based SARS-CoV-2 detection in Canadian laboratories. J Clin Virol, 2020. 128: p. 104433.
20. Laureano, A.F.S. and M. Riboldi, The different tests for the diagnosis of COVID-19 – A review in Brazil so far. JBRA Assist Reprod, 2020. 24(3): p. 340-346.
21. EUnetHTA, What is the diagnostic accuracy of molecular methods that detect the presence of the SARS-CoV-2 virus in people with suspected COVID-19 Project ID: RCROT02 2020. https://eunethta.eu/wp-content/uploads/2020/07Project_Plan_RCROT02_Molecular_Methods_31.07.2020_final.pdf.
22. Whitman, J.D., et al., Evaluation of SARS-CoV-2 serology assays reveals a range of test performance. Nat Biotechnol, 2020.
23. EUnetHTA, RAPID COLLABORATIVE REVIEW ON THE CURRENT ROLE OF ANTIBODY TESTS FOR NOVEL CORONAVIRUS SARS-COV-2 IN THE MANAGEMENT OF THE PANDEMIC. Project ID: RCR OT 01, 2020. https://eunethta.eu/wp-content/uploads/2020/06/RCR_OT_01-_Antibody-tests-for-SARSCoV-2_23-06-2020.pdf.
24. Lisboa Bastos, M., et al., Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis. BMJ, 2020. 370: p. m2516.
25. Deeks, J., et al., Antibody tests for identification of current and past infection with SARS-CoV-2 (Review). Cochrane Database of Systematic Reviews 2020, 2020. DOI: 10.1002/14651858.CD013652.
26. Doshi, P., Covid-19: Do many people have pre-existing immunity? BMJ, 2020. 370: p. m3563.
27. Guglielmi, G., Fast coronavirus tests: what they can and can’t do. Nature, 2020. 585(doi: 10.1038/d41586-020-02661-2): p. 496-498
28. Song, J.W., et al., Immunological and inflammatory profiles in mild and severe cases of COVID-19. Nat Commun, 2020. 11(1): p. 3410.
29. Laing, A.G., et al., A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med, 2020.
30. Hadjadj, J., et al., Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science, 2020. 369(6504): p. 718-724.