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Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(10): 1175-1180, 2021 Oct.
Article in Chinese | MEDLINE | ID: covidwho-1600033

ABSTRACT

OBJECTIVE: To analyze the data of Chinese medicine prescriptions for the treatment of coronavirus disease 2019 (COVID-19) in Shijiazhuang City, Hebei Province, with a view to further guide the clinical use of Chinese medicine in the prevention and treatment of COVID-19. METHODS: Forty-eight patients diagnosed with COVID-19 who were treated by critical care team of Hebei Traditional Chinese Medicine Hospital in the intensive care unit (ICU) of Hebei Chest Hospital (Hebei Provincial COVID-19 designated hospital) from January 7 to March 4, 2021, were enrolled in this study. The patients' gender, age, clinical classification, past history, and all Chinese medicine prescriptions for the first visit and follow-up visits during the hospitalization were collected. A database was established based on the Ancient and Modern Medical Records Cloud Platform (V2.2.1), and the methods of frequency analysis, correlation analysis, cluster analysis, and complex network analysis were used to analyze the prescriptions of traditional Chinese medicine. RESULTS: Among the 48 patients with COVID-19, 20 were males and 28 were females; the average age was (62.4±13.7) years old. The patients' condition was generally severe, including 17 cases of common type, 25 cases of severe type, and 6 cases of critical type, most of whom were combined with hypertension, coronary heart disease, diabetes, chronic obstructive pulmonary disease and other basic illnesses. A total of 146 valid prescriptions were included, involving 59 prescriptions and 115 Chinese medicines. Frequency analysis of 146 prescriptions showed that the commonly used prescriptions for patients with COVID-19 were Qingfei Paidu decoction (30 times, 20.55%), Xuanbai Chengqi decoction (10 times, 6.85%), and Dayuan Yin (10 times, 6.85%). The common Chinese medicines were liquorice (80 times, 54.79%), tuckahoe (76 times, 52.05%), gypsum (70 times, 47.95%), bitter almond (70 times, 47.95%), ephedra (57 times, 39.04%), scutellaria (56 times, 38.36%), tangerine peel (53 times, 36.30%), patchouli (50 times, 34.25%), atractylodes macrocephala (50 times, 34.25%), and bupleurum (43 times, 29.45%). The main effects were clearing heat and detoxification (129 times), clearing heat-fire (129 times) and eliminating dampness and diuresis (110 times). The medicinal properties were mainly warm (509 times), flat (287 times), and cold (235 times). The medicinal tastes were mainly pungent (765 times), sweet (654 times), and bitter (626 times). The medicinal channel tropism were mainly lung (1 096 times), spleen (785 times), and stomach (687 times). The correlation analysis showed that there were 17 drug combinations in total, among which the top 3 drug pairs in support were bitter almond-gypsum (0.43), ephedra-bitter almond (0.38), tangerine peel-poria (0.36), and ephedra-gypsum (0.36). Cluster analysis showed that there were 3 groups of clustering formulas. The first group was ephedra, bitter almond, and gypsum. The second group was patchouli, tuckahoe, tangerine peel, and atractylodes macrocephala. The third group was scutellaria, licorice, immature orange fruit, oriental waterplantain rhizome, bupleurum, ginger, and cassia twig. The core drugs were composed of tuckahoe, bupleurum, tangerine peel, atractylodes macrocephala, patchouli, bitter almond, scutellaria, gypsum, ephedra, and licorice. CONCLUSIONS: Middle-aged and elderly patients with COVID-19 are accompanied by Qi deficiency and internal invasion of toxins, and the pathogenesis evolves rapidly. Damp and turbid toxins often block the lungs and trap the spleen, leading to disorder of Qi movement, and even invaginate Ying and Xue, drain Yin and Yang. The treatment is based on removing turbidity and detoxification, and replenishing Qi and nourishing Yin are the principle treatments, so that the evil is eliminated and the Qi is restored.


Subject(s)
COVID-19 Drug Treatment , Drugs, Chinese Herbal , Medicine, Chinese Traditional , Aged , Data Mining , Female , Humans , Intensive Care Units , Male , Middle Aged
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