tetano
Editor, Senior Moderator
Sci Rep
. 2021 Oct 5;11(1):19713.
doi: 10.1038/s41598-021-99027-z.
Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue
Tun-Linn Thein[SUP] #[/SUP][SUP] 1 [/SUP], Li Wei Ang[SUP] #[/SUP][SUP] 1 [/SUP], Barnaby Edward Young[SUP] 1 2 3 [/SUP], Mark I-Cheng Chen[SUP] 1 4 [/SUP], Yee-Sin Leo[SUP] 5 6 7 8 9 [/SUP], David Chien Boon Lye[SUP] 10 11 12 13 [/SUP]
Affiliations
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.
. 2021 Oct 5;11(1):19713.
doi: 10.1038/s41598-021-99027-z.
Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue
Tun-Linn Thein[SUP] #[/SUP][SUP] 1 [/SUP], Li Wei Ang[SUP] #[/SUP][SUP] 1 [/SUP], Barnaby Edward Young[SUP] 1 2 3 [/SUP], Mark I-Cheng Chen[SUP] 1 4 [/SUP], Yee-Sin Leo[SUP] 5 6 7 8 9 [/SUP], David Chien Boon Lye[SUP] 10 11 12 13 [/SUP]
Affiliations
- PMID: 34611200
- DOI: 10.1038/s41598-021-99027-z
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.