@Article{info:doi/10.2196/20108,
author="Liu, Dong
and Wang, Yuyan
and Wang, Juan
and Liu, Jue
and Yue, Yongjie
and Liu, Wenjun
and Zhang, Fuhai
and Wang, Ziping",
title="Characteristics and Outcomes of a Sample of Patients With COVID-19 Identified Through Social Media in Wuhan, China: Observational Study",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="13",
volume="22",
number="8",
pages="e20108",
keywords="COVID-19; risk factors; web-based data; outcome; infectious disease; clinical characteristic; mortality; social media; prognosis, China; coronavirus",
abstract="Background: The number of deaths worldwide caused by coronavirus disease (COVID-19) is increasing rapidly. Information about the clinical characteristics of patients with COVID-19 who were not admitted to hospital is limited. Some risk factors of mortality associated with COVID-19 are controversial (eg, smoking). Moreover, the impact of city closure on mortality and admission rates is unknown. Objective: The aim of this study was to explore the risk factors of mortality associated with COVID-19 infection among a sample of patients in Wuhan whose conditions were reported on social media. Methods: We enrolled 599 patients with COVID-19 from 67 hospitals in Wuhan in the study; 117 of the participants (19.5{\%}) were not admitted to hospital. The demographic, epidemiological, clinical, and radiological features of the patients were extracted from their social media posts and coded. Telephone follow-up was conducted 1 month later (between March 15 and 23, 2020) to check the clinical outcomes of the patients and acquire other relevant information. Results: The median age of patients with COVID-19 who died (72 years, IQR 66.5-82.0) was significantly higher than that of patients who recovered (61 years, IQR 53-69, P<.001). We found that lack of admission to hospital (odds ratio [OR] 5.82, 95{\%} CI 3.36-10.1; P<.001), older age (OR 1.08, 95{\%} CI 1.06-1.1; P<.001), diffuse distribution (OR 11.09, 95{\%} CI 0.93-132.9; P=.058), and hypoxemia (odds ratio 2.94, 95{\%} CI 1.32-6.6; P=.009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (OR 0.9, 95{\%} CI 0.44-1.85; P=.78). Conclusions: Older age, diffuse distribution, and hypoxemia are factors that can help clinicians identify patients with COVID-19 who have poor prognosis. Our study suggests that aggregated data from social media can also be comprehensive, immediate, and informative in disease prognosis. ",
issn="1438-8871",
doi="10.2196/20108",
url="http://www.jmir.org/2020/8/e20108/",
url="https://doi.org/10.2196/20108",
url="http://www.ncbi.nlm.nih.gov/pubmed/32716901"
}