Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction–Based SARS-CoV-2 Tests by Time Since Exposure

Background: Tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on reverse transcriptase polymerase chain reaction (RT-PCR) are being used to “rule out” infection among high-risk persons, such as exposed inpatients and health care workers. It is critical to understand how the predictive value of the test varies with time from exposure and symptom onset to avoid being falsely reassured by negative test results. Objective: To estimate the false-negative rate by day since infection. Design: Literature review and pooled analysis. Setting: 7 previously published studies providing data on RT-PCR performance by time since symptom onset or SARS-CoV-2 exposure using samples from the upper respiratory tract (n = 1330). Patients: A mix of inpatients and outpatients with SARS-CoV-2 infection. Measurements: A Bayesian hierarchical model was fitted to estimate the false-negative rate by day since exposure and symptom onset. Results: Over the 4 days of infection before the typical time of symptom onset (day 5), the probability of a false-negative result in an infected person decreases from 100% (95% CI, 100% to 100%) on day 1 to 67% (CI, 27% to 94%) on day 4. On the day of symptom onset, the median false-negative rate was 38% (CI, 18% to 65%). This decreased to 20% (CI, 12% to 30%) on day 8 (3 days after symptom onset) then began to increase again, from 21% (CI, 13% to 31%) on day 9 to 66% (CI, 54% to 77%) on day 21. Limitation: Imprecise estimates due to heterogeneity in the design of studies on which results were based. Conclusion: Care must be taken in interpreting RT-PCR tests for SARS-CoV-2 infection—particularly early in the course of infection—when using these results as a basis for removing precautions intended to prevent onward transmission. If clinical suspicion is high, infection should not be ruled out on the basis of RT-PCR alone, and the clinical and epidemiologic situation should be carefully considered. Primary Funding Source: National Institute of Allergy and Infectious Diseases, Johns Hopkins Health System, and U.S. Centers for Disease Control and Prevention.

[1]  W Leisenring,et al.  A marginal regression modelling framework for evaluating medical diagnostic tests. , 1997, Statistics in medicine.

[2]  S. Glynn,et al.  Dynamics of viremia in early hepatitis C virus infection , 2005, Transfusion.

[3]  Bernhard P. Konrad,et al.  On the duration of the period between exposure to HIV and detectable infection. , 2017, Epidemics.

[4]  Hannah R. Meredith,et al.  The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application , 2020, Annals of Internal Medicine.

[5]  J. Desenclos,et al.  Cluster of coronavirus disease 2019 (Covid-19) in the French Alps, 2020 , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[6]  N. Kim,et al.  Clinical Course and Outcomes of Patients with Severe Acute Respiratory Syndrome Coronavirus 2 Infection: a Preliminary Report of the First 28 Patients from the Korean Cohort Study on COVID-19 , 2020, Journal of Korean medical science.

[7]  T. Petersen,et al.  Can Pediatric COVID-19 Testing Sensitivity Be Improved With Sequential Tests? , 2020, Anesthesia and analgesia.

[8]  S. Basu,et al.  Frequency of routine testing for SARS-CoV-2 to reduce transmissionamong workers , 2020, medRxiv.

[9]  Cecile Viboud,et al.  Estimating the early death toll of COVID-19 in the United States , 2020, medRxiv.

[10]  Victor M Corman,et al.  Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[11]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[12]  D. Levy-bruhl,et al.  Cluster of Coronavirus Disease 2019 (COVID-19) in the French Alps, February 2020 , 2020 .

[13]  S. Lother Preoperative SARS-CoV-2 screening: Can it really rule out COVID-19? , 2020, Canadian Journal of Anesthesia/Journal canadien d'anesthésie.

[14]  Marius Ueffing,et al.  [Seroprevalence and SARS-CoV-2 testing in healthcare occupations]. , 2020, Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft.

[15]  P. Vollmar,et al.  Virological assessment of hospitalized patients with COVID-2019 , 2020, Nature.

[16]  A preliminary study on serological assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 238 admitted hospital patients , 2020, Microbes and Infection.

[17]  [Tell me, where the Evidence is?] , 2020, Rheuma plus.

[18]  Lei Liu,et al.  A preliminary study on serological assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 238 admitted hospital patients , 2020, Microbes and Infection.

[19]  Catherine M. Brown,et al.  First 12 patients with coronavirus disease 2019 (COVID-19) in the United States , 2020, medRxiv.

[20]  S. Lauer,et al.  Vibrio cholerae O1 transmission in Bangladesh: insights from a nationally representative serosurvey , 2020, The Lancet. Microbe.

[21]  Vibrio cholerae O1 transmission in Bangladesh: insights from a nationally representative serosurvey , 2020, The Lancet. Microbe.

[22]  Lei Liu,et al.  Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 , 2020, medRxiv.

[23]  Qi Jin,et al.  Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19) , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[24]  T. Lancet,et al.  COVID-19: protecting health-care workers , 2020, The Lancet.

[25]  B. Salzberger,et al.  Epidemiologie von SARS-CoV-2-Infektion und COVID-19 , 2020, Der Internist.

[26]  Yongsheng Wu,et al.  Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts , 2020, medRxiv.