Evidence map›Paper›PMID 34611200›Full record

ArticleScientific reports2021

Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue.

Tun-Linn Thein, Li Wei Ang, Barnaby Edward Young, Mark I-Cheng Chen, Yee-Sin Leo, David Chien Boon Lye

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
6.9field-weighted citation impact, top 3% 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.

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

13 citing papers in PubMed, 38 citations in OpenAlex.

  1. Review
  2. Article
  3. Dengue Mimickers: Which Clinical Conditions Can Resemble Dengue Fever?Revista da Sociedade Brasileira de Medicina Tropical · 2024
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. A prospective evaluation of diagnostic performance of a combo rapid antigen test QuickNavi-Flu+COVID19 Ag.Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy · 2022
    Article
  9. Article
  10. Coinfection and cross-reaction of dengue and COVID-19: a case series analysis.Revista da Sociedade Brasileira de Medicina Tropical · 2022
    Article
  11. Article
  12. Article
  13. 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

6 authors at 2 institutions in 1 country.

Tun-Linn Thein *National Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore.
Li Wei Ang *National Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore.
Barnaby Edward YoungNational Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore.
Mark I-Cheng ChenNational Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore.
Yee-Sin LeoNational Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore. Yee_Sin_Leo@ncid.sg.
David Chien Boon LyeNational Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore. David_Lye@ncid.sg.
National Centre for Infectious Diseases · SGNational University Health System · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel coronavirus disease 2019 (COVID-19) presents with non-specific clinical features. This may result in misdiagnosis or delayed diagnosis, and lead to further transmission in the community. We aimed to derive early predictors to differentiate COVID-19 from influenza and dengue. The study comprised 126 patients with COVID-19, 171 with influenza and 180 with dengue, who presented within 5 days of symptom onset. All cases were confirmed by reverse transcriptase polymerase chain reaction tests. We used logistic regression models to identify demographics, clinical characteristics and laboratory markers in classifying COVID-19 versus influenza, and COVID-19 versus dengue. The performance of each model was evaluated using receiver operating characteristic (ROC) curves. Shortness of breath was the strongest predictor in the models for differentiating between COVID-19 and influenza, followed by diarrhoea. Higher lymphocyte count was predictive of COVID-19 versus influenza and versus dengue. In the model for differentiating between COVID-19 and dengue, patients with cough and higher platelet count were at increased odds of COVID-19, while headache, joint pain, skin rash and vomiting/nausea were indicative of dengue. The cross-validated area under the ROC curve for all four models was above 0.85. Clinical features and simple laboratory markers for differentiating COVID-19 from influenza and dengue are identified in this study which can be used by primary care physicians in resource limited settings to determine if further investigations or referrals would be required.

Indexed as

AdultArea Under CurveCohort StudiesCOVID-19DengueDiagnosis, DifferentialDiarrheaFemaleFeverHumansInfluenza, HumanLymphocyte CountMaleMiddle AgedPlatelet CountRNA, ViralRNA, Viral

Identifiers

PMID34611200
PMCPMC8492678
OpenAlexW3116588491

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.