Working PaperArticleVersion 1This version is not peer-reviewed
Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis
Version 1
: Received: 14 January 2021 / Approved: 15 January 2021 / Online: 15 January 2021 (12:44:59 CET)
How to cite:
Chen, K.; Yunusa, I.; Liu, S.; Wu, C.; Yeh, C.; Han, S.; Mao, C.; Harika, D.; Rothman, R.; Pekosz, A. Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis. Preprints2021, 2021010290
Chen, K.; Yunusa, I.; Liu, S.; Wu, C.; Yeh, C.; Han, S.; Mao, C.; Harika, D.; Rothman, R.; Pekosz, A. Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis. Preprints 2021, 2021010290
Chen, K.; Yunusa, I.; Liu, S.; Wu, C.; Yeh, C.; Han, S.; Mao, C.; Harika, D.; Rothman, R.; Pekosz, A. Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis. Preprints2021, 2021010290
APA Style
Chen, K., Yunusa, I., Liu, S., Wu, C., Yeh, C., Han, S., Mao, C., Harika, D., Rothman, R., & Pekosz, A. (2021). Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis. Preprints. https://doi.org/
Chicago/Turabian Style
Chen, K., Richard Rothman and Andrew Pekosz. 2021 "Distribution of Symptoms, Comorbidities, and Severity Among Patients with COVID-19 by Age Groups, Countries, and Stages of Outbreak: A Systematic Review and Meta-Analysis" Preprints. https://doi.org/
Abstract
Study objective Since December 2019, the coronavirus disease (COVID-19) pandemic has caused over a million deaths and resulted in adverse socio-economic impacts worldwide. However, predictability and prognostication of clinical features vary among different populations. Methods We search PubMed, EMBASE, Cochrane Library, Google Scholar, and WHO Global Health Library from December 2019 to April 2020 for studies reporting the risk factors, clinical features, and outcomes. The random-effect models for transformed prevalence (single-arm) or bivariate random-effect models (sensitivity and specificity) for correlated performance indicators. Results Among the 189 included studies representing 53,659 patients, the most sensitive predictor for COVID-19 infection was fever in adults (83%, 95% confidence interval [CI]:73–90%), and the most specific predictor was fatigue (96%, 95% CI: 80–99%). Fever was the most sensitive symptom in predicting the severity (89%, 95% CI:83–92%), followed by cough (71%, 95% CI:63–78%). The most specific predictor of severe COVID-19 was a chronic obstructive pulmonary disease (99%, 95% CI:98–99%). The stage of the outbreak and age significantly affect the prevalence of fever, fatigue, cough, and dyspnea. Fever, cough, fatigue, hypertension, and diabetes mellitus combined have a 3.06 positive likelihood ratio (PLR) and a 0.59 negative likelihood ratio (NLR) in the diagnosis. Additionally, fever, cough, sputum production, myalgia, fatigue, and dyspnea combined have a 10.44 PLR and a 0.16 NLR in predicting severe COVID-19. Conclusions Understanding the different distribution of predictors essential for screening potential COVID-19 infection and severe outcomes and the combination of symptoms could improve the pre-test probability.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
