Characteristics and Outcomes of a Sample of Patients With COVID-19 Identified Through Social Media in Wuhan, China: Observational Study (Preprint)

Author:

Liu DongORCID,Wang YuyanORCID,Wang JuanORCID,Liu JueORCID,Yue YongjieORCID,Liu WenjunORCID,Zhang FuhaiORCID,Wang ZipingORCID

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, <i>P</i>&lt;.001). We found that lack of admission to hospital (odds ratio [OR] 5.82, 95% CI 3.36-10.1; <i>P</i>&lt;.001), older age (OR 1.08, 95% CI 1.06-1.1; <i>P</i>&lt;.001), diffuse distribution (OR 11.09, 95% CI 0.93-132.9; <i>P</i>=.058), and hypoxemia (odds ratio 2.94, 95% CI 1.32-6.6; <i>P</i>=.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; <i>P</i>=.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.

Publisher

JMIR Publications Inc.

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