tetano
Editor, Senior Moderator
J Med Internet Res
. 2020 Jul 26.
doi: 10.2196/20108. Online ahead of print.
Characteristics and Outcomes of 599 Patients Infected with COVID-19 in Wuhan, China: Based on an Online Reported Sample
Dong Liu[SUP] 1 [/SUP], Yuyan Wang[SUP] 2 [/SUP], Juan Wang[SUP] 3 [/SUP], Jue Liu[SUP] 4 [/SUP], Yongjie Yue[SUP] 1 [/SUP], Wenjun Liu[SUP] 1 [/SUP], Ziping Wang[SUP] 2 [/SUP]
Affiliations
Abstract
Background: The number of global deaths caused by coronavirus disease of 2019 (COVID-19) is increasing fast. Information about the clinical characteristics of COVID-19 patients who were not admitted to the hospital is limited. Some risk factors of mortality associated with COVID-19 were controversial (smoking, etc.). The effect of city closure on mortality and admission rates was unknown.
Objective: To explore the risk factors of mortality associated with COVID-19 infection with an online reported sample in Wuhan.
Methods: 599 COVID-19 cases from 67 hospitals in Wuhan were enrolled in the study (117 were not admitted to hospital). Their demographic, epidemiological, clinical, and radiological features were extracted and coded from their online report. The telephone follow-up was conducted one month later (between March 15 and 23) to check the clinical outcomes of the patients and acquire other relevant information.
Results: The median age of deceased patients (72 years, IQR 66.5-82.0) was significantly older than recovered patients (61 years, IQR 53-69, p<.001). We found unable to be admitted to hospital (odds ratio 5.82, 95% CI, 3.36-10.1; p<.001 ), older age (odds ratio 1.08, 95% CI, 1.06-1.1; p<.001), diffuse distribution (odds ratio 11.09, 95% CI, 0.93-132?9; p=.058), hypoxemia (odds ratio 2.94, 95% CI, 1.32-6.6; p=0?009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (odds ratio 0.9, 95% CI, .44-1.85; p=0?781).
Conclusions: Older age, diffuse distribution, hypoxemia can be used as factors that help clinicians identify patients with poor prognosis. Our study suggests aggregated data from social media can also be comprehensive, immediate, and informative.
. 2020 Jul 26.
doi: 10.2196/20108. Online ahead of print.
Characteristics and Outcomes of 599 Patients Infected with COVID-19 in Wuhan, China: Based on an Online Reported Sample
Dong Liu[SUP] 1 [/SUP], Yuyan Wang[SUP] 2 [/SUP], Juan Wang[SUP] 3 [/SUP], Jue Liu[SUP] 4 [/SUP], Yongjie Yue[SUP] 1 [/SUP], Wenjun Liu[SUP] 1 [/SUP], Ziping Wang[SUP] 2 [/SUP]
Affiliations
- PMID: 32716901
- DOI: 10.2196/20108
Abstract
Background: The number of global deaths caused by coronavirus disease of 2019 (COVID-19) is increasing fast. Information about the clinical characteristics of COVID-19 patients who were not admitted to the hospital is limited. Some risk factors of mortality associated with COVID-19 were controversial (smoking, etc.). The effect of city closure on mortality and admission rates was unknown.
Objective: To explore the risk factors of mortality associated with COVID-19 infection with an online reported sample in Wuhan.
Methods: 599 COVID-19 cases from 67 hospitals in Wuhan were enrolled in the study (117 were not admitted to hospital). Their demographic, epidemiological, clinical, and radiological features were extracted and coded from their online report. The telephone follow-up was conducted one month later (between March 15 and 23) to check the clinical outcomes of the patients and acquire other relevant information.
Results: The median age of deceased patients (72 years, IQR 66.5-82.0) was significantly older than recovered patients (61 years, IQR 53-69, p<.001). We found unable to be admitted to hospital (odds ratio 5.82, 95% CI, 3.36-10.1; p<.001 ), older age (odds ratio 1.08, 95% CI, 1.06-1.1; p<.001), diffuse distribution (odds ratio 11.09, 95% CI, 0.93-132?9; p=.058), hypoxemia (odds ratio 2.94, 95% CI, 1.32-6.6; p=0?009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (odds ratio 0.9, 95% CI, .44-1.85; p=0?781).
Conclusions: Older age, diffuse distribution, hypoxemia can be used as factors that help clinicians identify patients with poor prognosis. Our study suggests aggregated data from social media can also be comprehensive, immediate, and informative.