摘要
目的探讨原发性支气管肺癌(简称肺癌)中医证候分布规律和方药使用特点。方法检索1977年1月至2013年7月中国期刊全文数据库(CNKI)收录的中医及中西医结合治疗肺癌的临床研究和个人经验报告类文献,将纳入文献中的证型及治疗所用方剂、药物进行规范化处理,并分别对其进行频数分析、系统聚类分析、主成分分析、因子分析。结果纳入文献共24篇。肺癌中医证型共计56个,其中气阴两虚、阴虚内热、脾虚痰湿、痰瘀互结、气滞血瘀、痰热壅肺、热毒炽盛、肺脾气虚、痰湿蕴肺、肺肾阴虚为主要证型,占72.6%;肺癌70个频次较高症状可分为6类症状聚类证型,分别是气阴两虚证、痰热壅肺证、阴虚内热证、痰瘀互结证、肺脾气虚证、痰湿蕴肺证;病机要素以实性居多,占58.33%,主要为痰浊、火热、血瘀、水湿、气滞、热毒,虚性病机占41.67%,主要为阴虚和气虚;脏腑病位主要在肺、脾、肾、胃、肝。共用方剂1 162首,其中成方569首,以补益剂、治燥剂、祛痰剂、清热剂、理血剂为主;共涉及中药459味,以补虚药、清热药、化痰止咳平喘药、活血化瘀药、利水渗湿药、理气药最为常用;使用频次较高的54味中药可分为7个聚类方,分别适用于气阴两虚,阴虚内热,痰热壅肺,热毒炽盛,肝肾阴虚,痰瘀互结,气滞血瘀,脾虚痰湿证型的肺癌。结论肺癌中医证候以气阴两虚、阴虚内热、脾虚痰湿、痰瘀互结、气滞血瘀为主,组方用药以化痰除湿、清热解毒散结、行气化瘀、补益肺脾、益气养阴为主。
Objective To research the TCM syndrome distribution and formula characteristics of primary lung cancer. Methods The articles of clinical research and personal experience in treating primary lung cancer with TCM and integrative medicine from Juanury 1977 to July 2013 were searched in China National Knowledge Internet (CNKI). The syndromes, prescriptions and herbs were normalized and analyzed with frequency analysis, cluster a- nalysis, principal component analysis and factor analysis. Results Totally 24 articles were included. There were 56 syndromes and the common syndromes were qi-yin deficiency syndrome, syndrome of endogenous heat due to yindeficiency, syndrome of phlegm-dampness due to spleen deficiency, intermingled phlegm and blood stasis syndrome, qi stagnation and blood stasis syndrome, syndrome of phlegm-heat obstructing lung, fire-toxin syndrome, lung-spleen qi deficiency syndrome, syndrome of phlegm-dampness stagnating in lung and lung-kidney yin deficiency syndrome, accounting for 72.6%. The frequent 70 symptoms of primary lung cancer could be clustered into six syndromes such as qi-yin deficiency syndrome, syndrome of phlegm-heat obstructing lung, syndrome of endogenous heat due to yin de- ficiency, intermingled phlegm and blood stasis syndrome, lung-spleen qi deficiency syndrome and syndrome of phlegm-dampness stagnating in lung. The common pathogenesis factors were excessive pathogens such as turbid phlegm, fire-heat, blood stasis, water-damp, qi stagnation and heat-toxin, accounting for 58.33%. The deficient pathogens were yin deficiency and qi deficiency, accounting for 41.67%. Disease located mainly in the lungs, spleen, kidneys, stomach and liver. There were 1 162 formulas and 569 set formulas used. The commonly used for- mulas were supplementing formula, moistening formula, phlegm-expelling formula, heat-clearing formula and blood- rectifying formula. There were 459 herbs involved. The commonly used herbs were supplementing medicinal, heat- clearing medicinal, phlegm-transforming cough-suppressing panting-calming medicinal, blood-quickening stasis-trans- forming medicinal, diuresis and diffusing dampness medicinal and qi-rectifying medicinal. The frequent 54 herbs could be clustered into 7 formulas to treat primary lung cancer with qi-yin deficiency syndrome, yin-deficiency inner- heat syndrome, syndrome of phlegm-heat obstructing lung, fire-toxin syndrome, liver and kidney yin deficiency, in- termingled phlegm and blood stasis syndrome, qi stagnation and blood stasis syndrome and syndrome of phlegm- dampness due to spleen deficiency respectively Conclusion The common syndromes of primary lung cancer are qi-yin deficiency syndrome, syndrome of endogenous heat due to yin deficiency, syndrome of phlegm-dampness due to spleen deficiency, intermingled phlegm and blood stasis syndrome and qi stagnation and blood stasis syndrome. The commonly used formulas are phlegm-transforming dampness-removing formula, heat-clearing toxin-removing masses- resolving formula, qi-moving stasis-transforming formula, lung-spleen supplementing formula and qi-supplementing yin-nourishing formula
出处
《中医杂志》
CSCD
北大核心
2014年第13期1146-1151,共6页
Journal of Traditional Chinese Medicine
基金
国家留学回国人员基金资助项目(2007170)
河南省国际科技合作项目(104300510019)
河南省创新新型科技团队资助项目(2010-29)
河南省中医学博士后科技创新团队资助项目
郑州市技术研究与开发经费支持项目(10CXTD145)
河南中医学院科技创新团队资助项目(2010XCXT05)
关键词
肺癌
证候分布
方药特点
聚类分析
primary lung cancer
syndrome distribution
formula characteristics
cluster analysis