Estimate false-negative RT-PCR rates for SARS-CoV-2. A systematic review and meta-analysis

Abstract

Background: Molecular-based tests used to identify symptomatic or asymptomatic patients infected by SARS-CoV-2 are characterized by high specificity but scarce sensitivity, generating false-negative results. We aimed to estimate, through a systematic review of the literature, the rate of RT-PCR false negatives at initial testing for COVID-19.

Methods: We systematically searched Pubmed, Embase and CENTRAL as well as a list of reference literature. We included observational studies that collected samples from respiratory tract to detect SARS-CoV-2 RNA using RT-PCR, reporting the number of false-negative subjects and the number of final patients with a COVID-19 diagnosis. Reported rates of false negatives were pooled in a meta-analysis as appropriate. We assessed the risk of bias of included studies and graded the quality of evidence according to the GRADE method. All information in this article is current up to February 2021.

Results: We included 32 studies, enrolling more than 18,000 patients infected by SARS-CoV-2. The overall false-negative rate was 0.12 (95%CI from 0.10 to 0.14) with very low certainty of evidence. The impact of misdiagnoses was estimated according to disease prevalence; a range between 2 and 58/1,000 subjects could be misdiagnosed with a disease prevalence of 10%, increasing to 290/1,000 misdiagnosed subjects with a disease prevalence of 50%.

Conclusions: This systematic review showed that up to 58% of COVID-19 patients may have initial false-negative RT-PCR results, suggesting the need to implement a correct diagnostic strategy to correctly identify suspected cases, thereby reducing false-negative results and decreasing the disease burden among the population.

Keywords: RT-PCR; SARS-CoV-2; evidence; false negative.

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

© 2021 Stichting European Society for Clinical Investigation Journal Foundation. Published by John Wiley & Sons Ltd.

Figures

FIGURE 1

PRISMA flow diagram of the study selection process for this systematic review

FIGURE 2

Summary of risk of bias assessment with the QUADAS‐2 tool. The x‐axis represents the percentage of studies graded to a specific risk of bias: low, moderate or high risk of bias. The y‐axis represents the 4 domains that were graded: patient selection, index test, reference standard, flow and timing

FIGURE 3

Summary of risk of bias assessment with the NIH tool. The x‐axis represents the percentage of answers: yes, no, unclear. The y‐axis reported the 12 questions considered in the evaluation. Q1: Was the research question or objective in this paper clearly stated?; Q2: Was the study population clearly specified and defined?; Q3: Were all the subjects selected or recruited from the same or similar populations (including the same time period)?; Q4: Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants?; Q5: Were the cases consecutive?; Q6: Was a sample size justification, power description, or variance and effect estimates provided?; Q7: Was the intervention clearly described?; Q8: Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?; Q9: Were the people assessing the outcomes blinded to the participants’ exposures/interventions?; Q10: Was the length of follow‐up adequate?; Q11: Were the statistical methods well described?; Q12: Were the results well described?

FIGURE 4

Forest plots of the false‐negative rate of RT‐PCR for SARS‐CoV‐2 infection