Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153789
Title: Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue
Authors: Thein, Tun-Linn
Ang, Li Wei
Young, Barnaby Edward
Chen, Mark I-Cheng
Leo, Yee Sin
Lye, David Chien Boon
Keywords: Science::Medicine
Issue Date: 2021
Source: Thein, T., Ang, L. W., Young, B. E., Chen, M. I., Leo, Y. S. & Lye, D. C. B. (2021). Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue. Scientific Reports, 11(1), 19713-. https://dx.doi.org/10.1038/s41598-021-99027-z
Journal: Scientific Reports
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.
URI: https://hdl.handle.net/10356/153789
ISSN: 2045-2322
DOI: 10.1038/s41598-021-99027-z
Rights: © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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