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10.2196/20108

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32716901!7431239!32716901
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suck abstract from ncbi


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pmid32716901      J+Med+Internet+Res 2020 ; 22 (8): e20108
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  • Characteristics and Outcomes of a Sample of Patients With COVID-19 Identified Through Social Media in Wuhan, China: Observational Study #MMPMID32716901
  • Liu D; Wang Y; Wang J; Liu J; Yue Y; Liu W; Zhang F; Wang Z
  • J Med Internet Res 2020[Aug]; 22 (8): e20108 PMID32716901show ga
  • 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.
  • |*Betacoronavirus[MESH]
  • |*Coronavirus Infections[MESH]
  • |*Pandemics[MESH]
  • |*Pneumonia, Viral[MESH]
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |COVID-19[MESH]
  • |Child[MESH]
  • |Child, Preschool[MESH]
  • |China[MESH]
  • |Female[MESH]
  • |Hospitalization[MESH]
  • |Humans[MESH]
  • |Infant[MESH]
  • |Infant, Newborn[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Odds Ratio[MESH]
  • |Prognosis[MESH]
  • |Retrospective Studies[MESH]
  • |Risk Factors[MESH]
  • |SARS-CoV-2[MESH]
  • |Social Media[MESH]


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