摘要
目的:分析中药内服辅助治疗骨质疏松性椎体压缩骨折(OVCF)的用药规律。方法:检索中国知网、万方数据库中2010年1月-2020年7月发表的关于中药内服联合椎体成形术治疗OVCF的文献并提取其中药处方,建立数据库,分析用药的频次、归经和四气五味,并使用SPSS Statistics 22.0软件和SPSS modeler 18.0软件进行聚类分析和关联规则分析。结果:所纳入的174个处方涉及149味中药,以补虚药、活血化瘀药和祛风湿药为主,用药频次最多的是当归、熟地黄、淫羊藿和牛膝;药性以温、平居多,药味以甘、苦、辛为主,归经多归肝、肾、脾经。Aprior算法关联规则分析显示了6个核心药组。结论:中医辅助治疗OVCF的原则主要是补益肝肾、活血化瘀,尤其是补肾阳、壮筋骨。
Objective:To analyze the medication rule of Chinese materia medica in auxiliary treatment of osteoporotic vertebral compression fracture(OVCF).Methods:Retrieved the literature about Chinese materia medica combined with vertebroplasty in the treatment of OVCF which published in CNKI and Wanfang database from January 2010 to July 2020,and TCM prescriptions were extracted.The database was established to analyze the frequency of medication,meridian return,four properties and five flavors.Cluster analysis and association rule are carried out by using SPSS Statistics 22.0 and SPSS modeler 18.0 software.Results:Among the 174 prescriptions,149 Chinese materia medica were included,The most frequently used drugs were tonic medicine,blood activating medicine and rheumatism medicine.herbs used with high frequency were Danggui,Niuxi,Shudihuang and Yinyanghuo,et al.Four properties mainly consist of warmth,neutral,and the flavors was mainly sweet,bitter and spicy.Meridian entryse mainly consist of liver,,kidney and spleen.The association rule analysis of Aprior algorithm showed 6 core drug groups.Conclusion:Chinese materia medica in auxiliary treatment of OVCF are mainly tonyifying liver and kidney,promoting blood circulation and removing blood stasis,especially tonifying kidney-yang and strengthening muscles and bones.
作者
吴世鹏
李攀
曹金灿
孙润芳
Wu Shipeng;Li Pan;Cao Jincan;Sun Runfang(Shanxi University of Chinese Medicine,Taiyuan 030001,China;Shanxi Hospital of Traditional Chinese Medicine,Taiyuan 030001,China)
出处
《亚太传统医药》
2021年第6期156-160,共5页
Asia-Pacific Traditional Medicine
关键词
椎体骨折
中药
关联规则
用药规律
数据挖掘
Osteoporotic Vertebral Compression Fracture
Chinese Materia Medica
Association Rule
Medication Rules
Data Mining
