Evidence map›Paper›PMID 32301749›Full record

ArticleClinical chemistry and laboratory medicine2020

Analytical performances of a chemiluminescence immunoassay for SARS-CoV-2 IgM/IgG and antibody kinetics.

Andrea Padoan, Chiara Cosma, Laura Sciacovelli, Diego Faggian, Mario Plebani

Registry-linked trialOpen access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in Clinical chemistry and laboratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04473352 (Diagnostic Accuracy of COVID-19 Detection Tests in Different Body Fluids of Infected Men and Concordance With Viral Cultures), which is not on this map. Cited by 160 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 5 pooled it
7.5field-weighted citation impact, top 2% 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.

NCT04473352 unknown statusnot on this map

Diagnostic Accuracy of COVID-19 Detection Tests in Different Body Fluids of Infected Men and Concordance With Viral Cultures

Typeobservational_patient_registrySponsorNucleo De Pesquisa E Desenvolvimento De Medicamentos Da Universidade Federal Do CearaRan2020 to 2021Enrolled80ConditionsCovid19
3 · Its place in the literature

Who cites it

160 citing papers in PubMed, 5 syntheses or guidelines pooled it, 310 citations in OpenAlex.

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100 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Andrea PadoanDepartment of Laboratory Medicine, University Hospital of Padova, Padova, Italy.ORCID https://orcid.org/0000-0003-1284-7885
Chiara CosmaDepartment of Laboratory Medicine, University Hospital of Padova, Padova, Italy.
Laura SciacovelliDepartment of Laboratory Medicine, University Hospital of Padova, Padova, Italy.ORCID https://orcid.org/0000-0003-3156-1399
Diego FaggianDepartment of Laboratory Medicine, University Hospital of Padova, Padova, Italy.
Mario PlebaniDepartment of Laboratory Medicine, University Hospital of Padova, Padova, Italy.ORCID https://orcid.org/0000-0002-0270-1711
University of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Coronavirus disease 2019, abbreviated to COVID-19, represents an emerging health threat worldwide as, after initial reports in China, it has continued to spread rapidly. The clinical spectrum of the disease varies from mild to severe acute respiratory distress syndrome (ARDS). Moreover, many patients can be asymptomatic, thus increasing the uncertainty of the diagnostic work-up. Laboratory tests play a pivotal role in the diagnosis and management of COVID-19, the current gold standard being real-time reverse transcription polymerase chain reaction (rRT-PCR) on respiratory tract specimens. However, the diagnostic accuracy of rRT-PCR depends on many pre-analytical and analytical variables. The measurement of specific COVID-19 antibodies (both IgG and IgM) should serve as an additional, non-invasive tool for disease detection and management. Methods The imprecision of the MAGLUMI™ 2000 Plus 2019-nCov IgM and IgG assays (Snibe, Shenzhen, China) was assessed by adopting the Clinical and Laboratory Standards Institute (CLSI) EP15-A3 protocol. Linearity of dilution and recovery was evaluated by means of mixes of high-level pools and low-level pools of serum samples. Immunoglobulin time kinetics were evaluated using a series of serum samples, repeatedly collected from COVID-19-positive patients at different times, from <5 days up to 26-30 days. Results Findings at the analytical validation of the assay carried out according to the CLSI EP15-A3 guideline demonstrated that imprecision and repeatability were acceptable (repeatability was <4% and <6% for IgM and IgG, respectively, whilst intermediate imprecision was <6%). In addition, results of dilution and recovery studies were satisfactory. The kinetics of COVID-19 antibodies confirmed previously reported findings, showing a rapid increase of both IgM and IgG after 6-7 days from the symptom onset. IgG had 100% sensitivity on day 12, whilst 88% was the higher positive rate achieved for IgM after the same time interval. Conclusions The findings of this study demonstrate the validity of the MAGLUMI 2000 Plus CLIA assay for the measurement of specific IgM and IgG in sera of COVID-19 patients, and for obtaining valuable data on the kinetics of both (IgM and IgG) COVID-19 antibodies. These data represent a pre-requisite for the appropriate utilization of specific antibodies for the diagnosis and management of COVID-19 patients.

Indexed as

Antibodies, ViralBetacoronavirusChinaClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19HumansImmunoenzyme TechniquesImmunoglobulin GImmunoglobulin MKineticsLuminescent MeasurementsPandemicsPneumonia, ViralReal-Time Polymerase Chain ReactionSARS-CoV-2Antibodies, ViralImmunoglobulin GImmunoglobulin Manalytical performancesantibody kineticsCOVID-19COVID IgG and IgMrRT-PCRSARS-CoV-2

Identifiers

PMID32301749
OpenAlexW3017325113

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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.