Evidence map›Paper›PMID 34540238›Full record

ArticleRoyal Society open science2021

A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests.

Lirong Cao, Shi Zhao, Qi Li, Lowell Ling, William K K Wu, Lin Zhang, Jingzhi Lou, Marc K C Chong, Zigui Chen, Eliza L Y Wong and 4 more

Abstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
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

14 authors.

Lirong CaoJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID 0000-0002-5793-3878
Shi ZhaoJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID 0000-0001-8722-6149
Qi LiJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Lowell LingDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
William K K WuDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Lin ZhangDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Jingzhi LouJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Marc K C ChongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID 0000-0001-5610-1298
Zigui ChenDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Eliza L Y WongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Benny C Y ZeeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Matthew T V ChanDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Paul K S ChanDepartment of Microbiology, Stanley Ho Centre for Emerging Infectious Diseases, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Maggie H WangJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID 0000-0003-1223-4595

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.

Indexed as

chest computed tomographyCOVID-19multiple tests integrationreverse transcription–polymerase chain reactionSARS-CoV-2serological tests

Identifiers

PMID34540238
PMCPMC8441124

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.