In:
Frontiers in Medicine, Frontiers Media SA, Vol. 8 ( 2021-8-10)
Abstract:
Background: The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A. Methods: All patients in the study were diagnosed at Fuyang No. 2 People's Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models. Results: In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of & lt;3 respiratory symptoms, then a highest temperature on the first day of admission & lt;38°C. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of & lt;37°C, then a complete lack of respiratory symptoms. Conclusions: The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.
Type of Medium:
Online Resource
ISSN:
2296-858X
DOI:
10.3389/fmed.2021.673253
DOI:
10.3389/fmed.2021.673253.s001
DOI:
10.3389/fmed.2021.673253.s002
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2021
detail.hit.zdb_id:
2775999-4
