What every clinician needs to know; Interpreting COVID-19 Test Results

John Kiragu , RN(BScN) ,MPH Student at the School of Public Health ,University of Nairobi.

COVID-19 Disease is caused by the SARS-COV2 virus. By September 20, 2020, the disease had resulted in 646 deaths in Kenya out of the 954,417 deaths that have been reported world over in addition to total of 36,829 cases of COVID-19 out of the 30,675,675 COVID-19 infections reported globally(WHO, 2020). Since the Director-General of WHO, Dr. Adhanom Tedros Ghabreyesus, called for ‘test, test, test’ mantra for every suspected case of COVID-19, reverse transcriptase-polymerase chain reaction (RT-PCR) has been adopted as the gold standard for diagnosis of COVID-19 disease in Kenya and in the entire globe. This was guided by several RT-PCR protocols that were released by WHO to inform COVID-19 testing. A gold standard does not imply a 100% accurate test as this is impossible in practice. A gold standard test implies a test that serves as a benchmark on the best method available to carry out a test (Cardoso et al 2014).

How Accurate is a COVID-19 test?

Despite the RT PCR test for COVID-19 being regarded as the gold standard, it has several shortcomings including inconsistent results because of the sample quality variation related to the sample collection techniques, sample processing, and differences in the amplification of the recommended primers. For example, the RT-PCR kit designed by China’s CDC detects Nucleocapsid (N) and the Open Reading Frame ORF1ab proteins of the SARS- CoV-2 DNA material while the USA-CDC primers include 2019-nCOV_N1, 2019-nCOV_N2, and 2019-nCOV_N3 SARS-COV2 DNA material. Differences brought about by sample quality and sufficiency and the type of primers amplified for RT PCR testing has been associated with false-negative and inconsistent serial results(Oliveiera et al 2020).

Accuracy of a diagnostic or screening test is determined by two major factors, namely, the sensitivity of the test and its specificity. The specificity of the test is its ability to correctly classify a patient who is disease-free as negative. Therefore, specificity rules in disease because it correctly identifies a disease-free person while having the ability to show if there is the presence of a disease in a normal disease-free or low-prevalence population. The sensitivity of a test is its ability to correctly classify a patient having the disease as positive. Therefore, sensitivity is used to rule out disease because it is useful in situations where the disease is highly prevalent. Therefore, it correctly confirms the presence of disease upon suspicion.

Sensitivity and specificity are important in the context of COVID-19 because a test with the high specificity of COVID-19 means that it correctly rules in the disease in low prevalent populations groups while a test with high sensitivity for COVID-19 disease is one that rules out disease in a person or population groups where the disease prevalence is high. CDC (2020) estimates show that the sensitivity and specificity of RT-PCR test are sufficiently high. In particular, CDC (2020) estimates the specificity for RT PCR to be as high as 100% which means that the chances of obtaining a false-positive are very slim. In addition, the estimates for sensitivity for RT-PCR tests is at 84-91% which indicates that there is considerable risk of false negative. Therefore, clinicians need to use their pretesting probability of the disease or the clinical suspicion for isolation of suspected cases despite a negative COVID-19 test result (Parikh et al 2008).  In our opinion therefore, in a situation of low prevalence of COVID-19 disease we require a COVID-19 test that has high specificity to rule in a COVID-19 case and facilitate prompt isolation and contact tracing for the identified case to halt emergence of an epidemic. In addition, given the risk of false negatives associated with the estimated sensitivity of RT PCR tests, there is need for heightened caution for patients who test negative for COVID-19.

How to interpret a COVID-19 test result?

How should clinicians interpret COVID-19 test results to their clients? According to Watson et al (2020) and the Handbook of COVID-19 Prevention and Treatment. 2020 based on studies in China and in line with WHO recommendation on testing for COVID-19 disease, Health Care workers should give the following health advice to their patient or clients:

First, COVID-19 tests are not 100% accurate. Second, a negative COVID-19 test result does not necessarily mean they are free from the disease. Third, a patient with clinical signs and symptoms and Imaging findings through Chest X-ray or CT scan suggestive of COVID-19 disease or illness should be isolated or recommended for close evaluation such as repeated COVID-19 tests while practicing strict infection prevention measures (Kiragu et al, 2020). Fourth, if the COVID-19 test result is positive then we are very confident that the patient is having COVID-19 viral infection and hence both the client and the clinicians should cooperate in adhering to infection prevention measures while facilitating care of the pateint.

COVID 19 positive and negative results have huge clinical implications because their interpretation by physicians and clinicians is fraught with base-rate fallacy and incorporation bias (Watson et al 2020) The base-rate fallacy occurs when there is a tendency to ignore general statistical information in preference of individuating specific information. This has an implication for COVID-19 clinical decision making because clinicians may ignore or may not have the ability to integrate all the existing general information about COVID-19 in their interpretation of an individual patient’s presentation and decisions of medical care. Incorporation bias occurs when the reference standard of a test is dependent on the diagnostic test and vice versa. For example, the clinical judgment of a COVID-19 diagnosis based on presenting features as per the case definition is used to predict RT-PCR COVID-19 positive results while the positive results are used to define COVID-19 disease presentations. In clinical practice, this can lead to the overestimation of the accuracy of the diagnosis of COVID-19 cases.

In Conclusion, we support the evidence-based argument by Watson and colleagues(2020) that negative COVID-19 results carry extreme risks in the clinical setup because they do not rule out the presence or absence of the disease given the imperfection of the sensitivity of the RT-PCR test. Therefore, health care workers and managers should consider serial COVID-19 testing protocol for inpatients who test negative for COVID-19 disease but has suggestive features of COVID-19 infection. In addition, in line with our earlier recommendations on the measures to protect health care workers from COVID-19 infection within the health care settings, there is a need for caution before allowing COVID-19 negative patients into non-COVID-19 wards until further considerations to rule in or rule out the disease are made.

RT PCR testing for COVID-19 is key to the identification of and response to the disease, however, the interpretation of the COVID-19 results is prone to clinician’s biases highlighted in this discussion which may distort true accuracy of the reference or diagnostic methods. In addition, the clinician’s understanding of the interpretation of COVID-19 results impacts on how and what advice they communicate to their patients and clients. Our advice is that a positive test result need to be communicated with more confidence than a negative test result for COVID-19.

References

  1. First Affiliated Hospital of Zhejiang University School of Medicine. Handbook of COVID-19 Prevention and Treatment. 2020. https://gmcc.alibabadoctor.com/prevention-manual
  2. Watson J, Whiting P, Brush J et al 2020. Interpreting a COVID-19 result. BMJ 2020; 369: m1808 doi: 10.1136/bmj.m1808 (Published 12 May 2020) at https://www.bmj.com/content/bmj/369/bmj.m1808.full.pdf

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John Kiragu

A student in Masters' of Public Health focusing on Health Economics, Health Policy and Planning at the School of Public Health_University of Nairobi.
Practicing RN with a Bachelors' Degree in Nursing Sciences from the same University. A founder for Kenyan-based Health-Tech Startup company implementing a digital platform to improve maternal health outcomes.

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