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To test or not to test for Covid-19? This question has plagued many, as the World Health Organization urged governments earlier this year to implement widespread testing of their citizens to curb the spread of the new coronavirus. According to an article published in the Medical Device Network, most of the tests that are performed today can be divided into polymerization chain reaction (PCR) or serological tests and none of them are 100% perfect. A PCR test uses a swab to take a sample from a patient’s nose or throat, and while it does not detect the virus itself, it does detect the presence of genetic material from the virus called RNA. In this article, Dr. Clare Craig argues that false positive results from a PCR test lead to false diagnoses, unnecessary measurements, and distort the overall picture of the pandemic. He adds that “it is important to direct testing to people who have symptoms that provide high clinical suspicion for the disease being evaluated. This subpopulation orientation due to their high false-positive rate is poor profiling. The more people in this subpopulation you target, the higher the false positive rate for the tests as a whole. “- Lindiwe Molekoa
A diagnostic error
By Dr. Clare Craig *
PCR testing for Covid-19 aims to detect people who have a high probability of being infectious. However, false positive test results lead to false diagnoses, unnecessary measurements, and distort the overall picture of the pandemic.
False positive PCR test results have more than one cause and productive discussions about them require the following 5 categories to be distinguished from each other.
the operating rate of false positives refers to the error rate in the whole process. This will vary from day to day, so the rate should be measured as a trend to an average, not as the minimum. Each laboratory will have its own operating false positive rate and this may change over time based on the following factors.
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Profiling errors
Who is being tested has a significant influence on the false positive rate. Any positive pregnancy test from, say, testing children in a reception class in elementary school must be a false positive. Similarly, tests for Covid-19 in asymptomatic people (for example, airport tests) are much more likely to produce false positive results than tests in symptomatic patients.
It so happens that some subpopulations within communities may have a higher false positive referral rate for unknown reasons. This is a frequent problem that we see, for example, in the detection of breast and cervical cancer in young women. In fact, this is why these screening programs do not screen young women. In the case of Covid-19, a similar unexpected level of false positives was observed in the summer in people in their 20s. When this subpopulation with a high false positive rate was discovered, they were subjected to further testing. We now know that they were false positive results because evidence from spring around the world shows that authentic Covid-19 outbreaks spread rapidly between age groups. This did not happen for the entire month of August, showing that the “outbreak” among young people was a pseudo-epidemic composed of false positives.
It is important to direct testing to people who have symptoms that give a high clinical suspicion for the disease being tested. This subpopulation orientation due to their high false-positive rate is poor profiling. The more people in this subpopulation you target, the higher the false positive rate for the test as a whole.
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Wrong identity
The probable underlying cause of the false positives in the youth was mistaken identity. When testing for RNA (the viral equivalent of DNA used for replication), the test must be able to distinguish between sequences that are unique to Covid-19 and sequences seen in other viruses or even human DNA. However, no test is perfect.
Human DNA has been mistaken for a different coronavirus when performing PCR tests. The human genome comprises three billion code letters. While neither of them can be an exact match to what the PCR test should detect, a close match could lead to errors in a proportion of the tests. This type of mistaken identity could lead to particular sub-populations being tested, creating profiling errors.
An outbreak of SARS-1 in 2003 in a nursing home in British Columbia turned out to be a common cold that caused coronavirus. Coronaviruses are a family of viruses, and while the Covid-19 virus spike protein is unique, the rest of the virus has many characteristics similar to other common colds. These similarities can lead to errors in PCR tests. Because coronaviruses are seasonal, this type of misidentity can cause a seasonal variation in the false positive rate.
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Contamination of the chain of evidence
There is a chain of evidence from the taking of the sample, to the delivery to the laboratory, the control of the samples and then the opening and work on them. Contamination can occur at any stage. This contamination can come from the people doing the work or from samples from other patients once in the lab.
Claims that PPE would be effective in preventing contamination of those who take the swabs, etc. It’s like claiming that wearing chainmail would prevent you from getting dirty on the beach. A delivery conductor that is post-infectious and spews RNA could contaminate the containers in which the samples are transported. Whoever opens those containers could transfer the RNA to the content. If the same gloves are used when opening multiple patient sample containers, the possibility of contamination between samples will be high. Many readers may have seen the disturbing footage of an undercover Dispatches reporter showing how some samples have been tampered with when they arrive at a lab.
Contamination is a problem in large part due to the nature of the test rather than careless handling. Having converted the RNA to DNA, the second step in the test is to multiply the DNA from one billion to one trillion times. That means that even with highly competent sample handling, the risk of contamination will remain because only the smallest piece of contaminating RNA can create a false positive test result. Reducing the number of times the DNA is multiplied reduces the chance of these errors, but not to zero.
The risk of cross-contamination from true positive samples will be higher when actual Covid is present among the samples being tested.
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Equipment errors
The test kit itself will have a fairly constant and low false positive rate. This is of the least importance, but every effort has been made to understand it. Calculation is possible based on retesting samples with different test kits. There seems to be a general misunderstanding that this is the only cause of false positive error and that because it is a low value there is no false positive problem.
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Burden of proof
In addition to choosing a reasonable cycle threshold to reduce contamination errors, other variations in the criteria used to determine positivity will lead to differences in the false positive rate. It is standard practice to test three genes that belong to the Covid-19 virus. However, if positive is defined as the presence of a single gene instead of all three, the false positive rate will be higher.
For example, the REACT study at Imperial carried out calibration between PCR tests in commercial laboratories and the same samples analyzed in Public Health England laboratories. They found a 57% false positive rate in May. To minimize this error, they used different criteria than those of commercial laboratories. Rather than report a gene at any threshold, they chose to define the presence of a gene below a cycle threshold of 37, or the presence of two genes, as positive.
The operating false positive rate is made up of five types of false positive errors: profiling errors; wrong identity; contamination errors; equipment errors and differences in the burden of proof. Changes in who the target is; Seasonal infections and laboratory quality standards can cause the false positive rate to change over time. The five types of false positives will vary between laboratories, so investigations of the rate in one laboratory cannot be extrapolated to another, and each has its own interaction with the underlying community prevalence rates. Therefore, the overall epidemiological false positive rate will vary depending on the location, time, and testing strategy.
- Clare Craig has been a pathologist since 2001 at the NHS in the UK and most recently as the day-to-day pathology leader for the cancer arm of Project 100,000 Genomes and working towards an AI startup in cancer diagnostics. Claire says she joined Panda because she believes that actions as a society have killed and continue to kill more people than Covid. As a diagnostic expert, Clare recognizes the mistakes that have been made and wants to yell at them, says Panda.
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