Selected article for: "Cox regression and high expression"

Author: Chen, Xiaoping; Hu, Wenjia; Ling, Jiaxin; Mo, Pingzheng; Zhang, Yongxi; Jiang, Qunqun; Ma, Zhiyong; Cao, Qian; Deng, Liping; Song, Shihui; Zheng, Ruiying; Gao, Shicheng; Ke, Hengning; Gui, Xien; Lundkvist, Ã ke; Li, Jinlin; Lindahl, Johanna F; Xiong, Yong
Title: Hypertension and Diabetes Delay the Viral Clearance in COVID-19 Patients
  • Cord-id: whnw19pc
  • Document date: 2020_3_24
  • ID: whnw19pc
    Snippet: ObjectivesComorbidities have significant indications for the disease outcome of COVID-19, however which underlying diseases that contribute the most to aggravate the conditions of COVID-19 patients is still largely unknown. SARS-CoV-2 viral clearance is a golden standard for defining the recovery of COVID-19 infections. To dissect the underlying diseases that could impact on viral clearance, we enrolled 106 COVID-19 patients who were hospitalized in the Zhongnan Hospital of Wuhan University, Wuh
    Document: ObjectivesComorbidities have significant indications for the disease outcome of COVID-19, however which underlying diseases that contribute the most to aggravate the conditions of COVID-19 patients is still largely unknown. SARS-CoV-2 viral clearance is a golden standard for defining the recovery of COVID-19 infections. To dissect the underlying diseases that could impact on viral clearance, we enrolled 106 COVID-19 patients who were hospitalized in the Zhongnan Hospital of Wuhan University, Wuhan, China between Jan 5 and Feb 25, 2020. MethodologyWe comprehensively analyzed demographic, clinical and laboratory data, as well as patient treatment records. Survival analyses with Kaplan-Meier and Cox regression modelling were employed to identify factors influencing the viral clearance negatively. ResultsWe found that increasing age, male gender, and angiotensin-converting enzyme 2 (ACE2) associated factors (including hypertension, diabetes, and cardiovascular diseases) adversely affected the viral clearance. Furthermore, analysis by a random forest survival model pointed out hypertension, cortisone treatment, gender, and age as the four most important variables. ConclusionsWe conclude that patients at old age, males, and/or having diseases associated with high expression of ACE2 will have worse prognosis during a COVID-19 infections.

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