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刘凤斌治疗重症肌无力用药规律的数据挖掘

Data Mining of LIU Feng-Bin’s Medication Rules for the Treatment of Myasthenia Gravis
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摘要 【目的】探讨刘凤斌教授治疗重症肌无力(myasthenia gravis,MG)的用药规律。【方法】收集刘凤斌教授门诊治疗MG患者的证型与处方资料,应用中医传承辅助平台(V2.5)中的描述性统计分析及关联规则分析功能,挖掘其核心用药和组方规律。【结果】所纳入的313例患者的临床医案可分为4个证型,以脾肾亏虚证为最常见。313首处方共包含中药174味,以补气药、补肾药、滋阴药为主,出现频次≥33次的常用中药共29味,频次居前10位的药物为五指毛桃、白术、黄芪、升麻、柴胡、千斤拔、玄参、茯苓、紫河车、菟丝子;所用药物药性主要为温性药,药味主要为甘味,主要归脾、肺、肾、肝、胃经。在置信度为0.6的条件下,设置支持度为10%、15%、20%,得到刘凤斌教授治疗MG中药处方的核心用药为30、20、16味。【结论】刘凤斌教授在秉承国医大师邓铁涛治疗MG经验的基础上,认为MG主要病机因脾胃虚损、肾精不足、气虚下陷致肌肉筋骨失养,治疗从脾、肾出发,以“补脾益肾、升阳举陷、强筋健骨”为治疗原则;以补中益气汤为基础方,注重“脾胃并治而有别”;配以石斛、玄参顾护胃阴,并重视“血肉有情以补肾”,加用紫河车补肾填精。 Objective To investigate the medication rules of Professor LIU Feng-Bin for the treatment of myasthenia gravis(MG).Methods The data of the syndrome types and prescriptions in the medical record for the MG patients treated by Professor LIU Feng-Bin in his outpatient clinic were collected,and the descriptive statistics and association rule analysis in the platform of Traditional Chinese Medicine Inheritance Support System TCMISS(V2.5)were applied to uncover the core drugs and prescription regularity for the treatment of MG.Results A total of 313 MG patients were included for the analysis.The clinical cases were differentiated as four syndrome types,and consumption of spleen and kidney was the predominated syndrome type.The 313 prescriptions for the cases included 174 herbal drugs,and the medicines were mainly classified into qi-replenishing drugs,kidney-tonifying drugs and yin-nourishing drugs.There were 29 commonly-used herbal drugs with the medication frequency being or over 33 times,and the top 10 frequently-used drugs were Fici Simplicissimae Radix,Atractylodis Macrocephalae Rhizoma,Astragali Radix,Cimicifugae Rhizoma,Bupleuri Radix,Flemingiae Radix et Caulis,Scrophulariae Radix,Poria,Hominis Placenta and Cuscutae Semen.The included drugs mainly had the medicinal properties of being warm,the flavor of being sweet,and the meridian tropism of the spleen,lung,kidney,liver and stomach meridians.When the confidence level was set at 0.6,and the support level was set at10%,15%and 20%respectively,a total of 30,20 and 16 core drugs were obtained respectively from the herbs in the prescriptions for MG treated by Professor LIU Feng-Bin.Conclusion Based on the experience in treating MG by traditional Chinese medicine master DENG Tie-Tao,Professor LIU Feng-Bin believes that the main pathogenesis of MG is due to the consumption of spleen and stomach,deficiency of kidney essence,sinking due to qi-deficiency,and failure of nourishment of muscles,sinews and bones.He advocates that the treatment should starts from the spleen and kidney,and follows the therapeutic principle of“replenishing spleen to tonify kidney,elevating yang to cure prolapse syndrome,and strengthening muscles and bones”.Professor LIU Feng-Bin adopts Buzhong Yiqi Decoction as the fundamental formula,emphasizing“treating the spleen and stomach simultaneously but differently”.Meanwhile,Dendrobii Caulis and Scrophulariae Radix are used together to protect and nourish the stomach yin,and Hominis Placenta is added for nourishing kidney to replenish marrow according to the theory of“medicinal with affinity to flesh and blood having the actions of nourishing kidney.
作者 黄远程 田燕华 邹雨杉 翁森辉 江其龙 李培武 刘凤斌(指导) HUANG Yuan-Cheng;TIAN Yan-Hua;ZOU Yu-Shan;WENG Sen-Hui;JIANG Qi-Long;LI Pei-Wu;LIU Feng-Bin(The First Clinical Medical School of Guangzhou University of Chinese Medicine,Guangzhou 510006 Guangdong,China;The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510405 Guangdong,China;Baiyun Hospital of the First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510470 Guangdong,China)
出处 《广州中医药大学学报》 CAS 2023年第1期203-208,共6页 Journal of Guangzhou University of Traditional Chinese Medicine
基金 国家自然科学基金项目(编号:81904133) 国家重点研发计划“中医药现代化”重点专项(2022YFC3501302) 刘凤斌广东省名中医传承工作室建设项目(粤中医办函[2020]1号) 广州中医药大学校科研团队培育项目(编号:2019KYTD101)。
关键词 重症肌无力 脾肾亏虚 补中益气汤 紫河车 数据挖掘 刘凤斌 myasthenia gravis consumption of spleen and kidney Buzhong Yiqi Decoction Hominis Placenta data mining LIU Feng-Bin
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