中药药理与临床

目的:基于数据挖掘探讨中药治疗类风湿性关节炎的组方用药规律。方法:以中国知网、万方数据库、维普数据库收录的期刊文献为资料来源，收集文献中所涉及的相关方剂，建立相应的数据库。使用Excel 2021和SPSS Statistic 25.0统计软件对符合纳入标准的方剂进行关联规则分析和因子分析。结果:共纳入130首方剂，共含142味中药，累计出现频数为1 480次。在纳入的统计数据中：频数≥22的中药共有20种，累计出现718次。前5味中药分别是当归(71次，9.87%)、甘草(63次，8.77%)、独活(51次，7.10%)、白芍(49次，6.82%)、防风(48次，6.69%);与其对应的清热药、解表药、补虚药在功效分类中出现最多，占比达到67.53%;药性药味中，药性以温、平的中药居多，药味以辛味、甘味的中药居多；归经分类中，归肝经、脾经、胃经的中药较多。类风湿性关节炎中医证型以风湿痹阻证和肝肾亏虚证占比最高。通过关联规则分析发现共有11种关联强度最高的药对组合，聚类分析可将药物分为4类，因子分析中7个公因子被提取出。结论:运用现代信息技术，将中医临床资料与大数据相结合进行规律探索，得出中药临床治疗类风湿性关节炎的规律：多使用补虚药，多选用入肝经的药物，最终反馈给临床，为指导临床更加安全合理用药及新处方研发提供依据。

Objective:To explore medication regularity of Chinese medicine in the treatment of rheumatoid arthritis based on data mining. Methods:The relevant prescriptions in the research articles retrieved from CNKI,Wanfang Data, and VIP were collected to establish a database. Excel 2021 and SPSS Statistic 25.0 statistical software were used for association analysis and factor analysis of Chinese medicinal prescriptions in line with the inclusion criteria. Results:A total of 130 prescriptions were obtained, involving 142 drugs with a total frequency of 1 480. According to the statistical data, there were 20 drugs with frequency ≥22,and the total frequency reached 718. The top 5 high-frequency drugs included ANGELICAE SINENSIS RADIX(71,9.87%),GLYCYRRHIZAE RADIX ET RHIZOMA(63,8.77%),ANGELICAE PUBESCENTIS RADIX(51,7.10%),PAEONIAE RADIX ALBA(49,6.82%),and SAPOSHNIKOVIAE RADIX(48,6.69%). In terms of efficacy, the heat-clearing, exterior-releasing, and deficiency-tonifying drugs accounted for a large proportion, reaching 67.53%. In terms of Yaoxing(药性),the majority of drugs were Wen(温) and Ping(平),and in terms of Yaowei(药味),drugs were mostly Xin(辛) and Gan(甘). In terms of Guijing(归经),most drugs acted on Ganjing(肝经),Pijing(脾经),and Weijing(胃经). In traditional Chinese medicine(TCM),rheumatoid arthritis was dominated by Fengshibizuzheng(风湿痹阻证) and Ganshenkuixuzheng(肝肾亏虚证). The association analysis revealed 11 drug combinations with strong association. Drugs were classified into four categories according to cluster analysis, and seven common factors were extracted by factor analysis. Conclusion:This study explored the medication regularity of Chinese medicine in the treatment of rheumatoid arthritis by virtue of TCM clinical data combined with big data with modern information technology. To be specific, the deficiency-tonifying drugs and those acting on Ganjing were frequently used. The above results were reported to the clinic, which is expected to provide a basis for clinical rational and safe medication and formulation of new prescriptions.