Dataset Information

A systematic review and meta-analysis of discharged COVID-19 patients retesting positive for RT-PCR.

ABSTRACT:

Background

With the increased number of patients discharged after having COVID-19, more and more studies have reported cases whose retesting was positive (RP) during the convalescent period, which brings a new public health challenge to the world.

Methods

We searched PubMed, Web of Science, The Cochrane Library, CNKI, WanFang and VIP from December 1, 2019 to December 31, 2020. The included studies were assessed using JBI critical appraisal tools and Newcastle-Ottawa Scale. The RP rate of discharge patients was analyzed by a meta-analysis. We adhered to PRISMA reporting guideline.

Findings

We have included 117 studies with 2669 RP participants after discharge. The methodological quality of 66 case reports were low to high, 42 case series and 3 cohort study were moderate to high, 3 case-control studies were moderate and 3 cross-sectional studies were low to moderate. The clinical manifestations of most RP patients were mild or asymptomatic, and CT imaging and laboratory examinations were usually normal. The existing risk factors suggest that more attention should be paid to sever patients, elderly patients, and patients with co-morbidities. The summary RP rate was 12·2% (95% CI 10·6-13·7) with high heterogeneity (I² = 85%).

Interpretation

To date, the causes and risk factors of RP result in discharged patients are not fully understood. High-quality etiological and clinical studies are needed to investigate these issues to further help us to make strategies to control and prevent its occurrence.

SUBMITTER: Ren X

PROVIDER: S-EPMC8052132 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA false-negative case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is defined as a person with suspected infection and an initial negative result by reverse transcription-polymerase chain reaction (RT-PCR) test, with a positive result on a subsequent test. False-negative cases have important implications for isolation and risk of transmission of infected people and for the management of coronavirus disease 2019 (COVID-19). We aimed to review and critically appraise evidence about the rate of RT-PCR false-negatives at initial testing for COVID-19.MethodsWe searched MEDLINE, EMBASE, LILACS, as well as COVID-19 repositories, including the EPPI-Centre living systematic map of evidence about COVID-19 and the Coronavirus Open Access Project living evidence database. Two authors independently screened and selected studies according to the eligibility criteria and collected data from the included studies. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the proportion of false-negative test results using a multilevel mixed-effect logistic regression model. The certainty of the evidence about false-negative cases was rated using the GRADE approach for tests and strategies. All information in this article is current up to July 17, 2020.ResultsWe included 34 studies enrolling 12,057 COVID-19 confirmed cases. All studies were affected by several risks of bias and applicability concerns. The pooled estimate of false-negative proportion was highly affected by unexplained heterogeneity (tau-squared = 1.39; 90% prediction interval from 0.02 to 0.54). The certainty of the evidence was judged as very low due to the risk of bias, indirectness, and inconsistency issues.ConclusionsThere is substantial and largely unexplained heterogeneity in the proportion of false-negative RT-PCR results. The collected evidence has several limitations, including risk of bias issues, high heterogeneity, and concerns about its applicability. Nonetheless, our findings reinforce the need for repeated testing in patients with suspicion of SARS-Cov-2 infection given that up to 54% of COVID-19 patients may have an initial false-negative RT-PCR (very low certainty of evidence).Systematic review registrationProtocol available on the OSF website: https://tinyurl.com/vvbgqya.

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