About: Hanshiyi Formula, an approved medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study

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About: Hanshiyi Formula, an approved medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Hanshiyi Formula, an approved medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study
Creator	Wang, Han Zhang, Ying Zhang, Lei Liu, Ai, Yanke Fengmei, Lian Gao, Zezheng Gou, Xiaowen He, Liyun Li, Xiuyang Lian, Lian, Fengmei Liu, Baoyan Tian, Jiaxing Tong, Xiaolin Wu, Haoran Yan, Shiyan Zheng, Yujiao
Source	Elsevier; Medline; PMC
abstract	We formulated a traditional Chinese medicine (TCM) prescription, Hanshiyi Formula (HSYF), which was approved and promoted by the Wuhan Municipal Health Commission for treating mild and moderate coronavirus disease 2019 (COVID-19). We aimed to evaluate the effect of HSYF on the progression to severe disease in mild and moderate COVID-19 patients. We conducted a retrospective cohort study of patients with mild and moderate COVID-19 in a quarantine station in Wuchang District, Wuhan. Using the real-time Internet information collection application and Centers for Disease Control for the Wuchang District, patient data were collected through patient self-reports and follow-ups. HSYF intervention was defined as the exposure. The primary outcome was the proportion of patients who progressed to a severe disease status, and a stratification analysis was performed. Univariate and multivariate regression analyses were performed to identify influencing factors that may affect the outcome. Further, we used propensity score matching (PSM) to assess the effect of HSYF intervention on the conversion of mild and moderate to a severe disease status. Totally, 721 mild and moderate COVID-19 patients were enrolled, including 430 HSYF users (exposed group) and 291 non-users (control group). No cases in the exposed group and 19 (6.5 %, P < 0.001) cases in the control group progressed to severe disease, and the difference between the two groups (exposed group-control group) was −6.5 % [95 % confidence interval (CI): (−8.87 %, −4.13 %)]. Univariate regression analysis revealed sex (male), age, fever, cough, and fatigue as risk factors for progression to severe disease. After PSM, none of the HSYF users and 7 (4.7 %, P = 0.022) non-users transitioned to severe disease, and the difference between the two groups (exposed group-control group) was −4.7 % [95 % CI: (−8.2 %, −1.2 %)]. Multivariate regression analysis revealed that sex (male) [OR: 3.145; 95 % CI: 1.036–9.545; P = 0.043] and age (> 48 years) [odds ratio (OR): 1.044; 95 % CI: 1.001–1.088; P = 0.044] were independent risk factors for conversion to severe disease. Therefore, HSYF can significantly reduce the progression to severe disease in patients with mild and moderate COVID-19, which may effectively prevent and treat the disease. However, further larger clinical studies are required to verify our results.
has issue date	2020-08-10(xsd:dateTime)
bibo:doi	10.1016/j.phrs.2020.105127
bibo:pmid	32791263
has license	no-cc
sha1sum (hex)	c7b1a86705269eeacd3a7fd14e4465d95ed0f5f8
schema:url	https://doi.org/10.1016/j.phrs.2020.105127
resource representing a document's title	Hanshiyi Formula, an approved medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study
has PubMed Central identifier	PMC7416080
has PubMed identifier	32791263
schema:publication	Pharmacol Res
resource representing a document's body	covid:c7b1a86705269eeacd3a7fd14e4465d95ed0f5f8#body_text
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