Evidence map›Paper›PMID 33301459›Full record

SynthesisPloS one2020

False-negative results of initial RT-PCR assays for COVID-19: A systematic review.

Ingrid Arevalo-Rodriguez, Diana Buitrago-Garcia, Daniel Simancas-Racines, Paula Zambrano-Achig, Rosa Del Campo, Agustin Ciapponi, Omar Sued, Laura Martinez-García, Anne W Rutjes, Nicola Low and 3 more

2 registry-linked trialsOpen access · goldAbstract readSystematic Review
In one paragraph

Synthesis in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 458 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
458citing papers in PubMed, 14 pooled it
68.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05175690 completednot on this mapstarted 2022, after this paper: background citation

Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness

TypeobservationalSponsorAudibleHealth AI, Inc.Ran2022 to 2022Enrolled1,126ConditionsCOVID19ArmsDiagnostic Software as Medical Device
NCT05364268 completednot on this mapstarted 2022, after this paper: background citation

Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness: A Prospective, Two-Arm Non-Inferiority Clinical Validation Trial of AudibleHealth Dx Software as a Medical Device (EUA-US)

TypeobservationalSponsorAudibleHealth AI, Inc.Ran2022 to 2022Enrolled514Conditions2019 Novel Coronavirus Disease, 2019 Novel Coronavirus Infection, 2019-nCoV Disease, COVID-19 PandemicArmsDiagnostic Test: Diagnostic Software as Medical Device
3 · Its place in the literature

Who cites it

458 citing papers in PubMed, 14 syntheses or guidelines pooled it, 693 citations in OpenAlex.

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  7. Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2022
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398 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 6 countries.

Ingrid Arevalo-RodriguezClinical Biostatistics Unit, Hospital Universitario Ramón y Cajal- IRYCIS, Madrid, Spain.ORCID 0000-0002-7326-4504
Diana Buitrago-GarciaInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Daniel Simancas-RacinesCentro de Investigación en Salud Pública y Epidemiología Clínica (CISPEC), Facultad de Ciencias de la Salud "Eugenio Espejo", Universidad UTE, Quito, Ecuador.
Paula Zambrano-AchigDepartment of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.
Rosa Del CampoDepartment of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.
Agustin CiapponiInstituto de Efectividad Clínica y Sanitaria (IECS-CONICET), Buenos Aires, Argentina.ORCID 0000-0001-5142-6122
Omar SuedFundación Huésped, Buenos Aires, Argentina.
Laura Martinez-GarcíaCIBER of Epidemiology and Public Health, Madrid, Spain.
Anne W RutjesInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Nicola LowInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.ORCID 0000-0003-4817-8986
Patrick M BossuytDepartment of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0003-4427-0128
Jose A Perez-MolinaInfectious Diseases Department, National Referral Centre for Tropical Diseases, Hospital Universitario Ramón y Cajal, Madrid, Spain.
Javier ZamoraClinical Biostatistics Unit, Hospital Universitario Ramón y Cajal- IRYCIS, Madrid, Spain.
Instituto Cajal · ESInstituto Ramón y Cajal de Investigación Sanitaria · ESUniversity of Bern · CHConsejo Nacional de Investigaciones Científicas y Técnicas · ARFundación Huésped · ARUniversidad UTE · ECUniversity of Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 REGISTRATION: Protocol available on the OSF website: https://tinyurl.com/vvbgqya.

Indexed as

COVID-19 Nucleic Acid TestingCOVID-19False Negative ReactionsHumansRNA, ViralSARS-CoV-2RNA, Viral

Identifiers

PMID33301459
PMCPMC7728293
OpenAlexW3111916089

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.