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1.云南中医药大学 临床医学院,昆明 650500
2.云南中医药大学 第一附属医院,昆明 650500
3.中国中医科学院 广安门医院,北京 100053
4.江西中医药大学 附属医院,南昌 330000
[第一作者] 骆始华,博士,主治医师,从事心血管疾病临床及基础研究,E-mail:luotuozlj@163.com
*王阶,博士,教授,从事中西医结合心血管病研究,Tel:010-62860501, E-mail:wangjie0103@yahoo.com.cn
纸质出版日期:2020-05-05,
网络出版日期:2019-11-18,
收稿日期:2019-10-24,
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骆始华, 李易, 赵丽娟, 等. 冠心病临界病变患者的中医证候分布规律[J]. 中国实验方剂学杂志, 2020,26(9):53-57.
Shi-hua LUO, Yi LI, Li-juan ZHAO, et al. Distribution Characteristics of Traditional Chinese Medicine Syndromes in Patients with Critical Lesions of Coronary Heart Disease[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2020,26(9):53-57.
骆始华, 李易, 赵丽娟, 等. 冠心病临界病变患者的中医证候分布规律[J]. 中国实验方剂学杂志, 2020,26(9):53-57. DOI: 10.13422/j.cnki.syfjx.20200524.
Shi-hua LUO, Yi LI, Li-juan ZHAO, et al. Distribution Characteristics of Traditional Chinese Medicine Syndromes in Patients with Critical Lesions of Coronary Heart Disease[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2020,26(9):53-57. DOI: 10.13422/j.cnki.syfjx.20200524.
目的:
2
探讨冠心病临界病变的中医证候分布规律特征,并初步确定证候分型及诊断标准,为本病的辨证论治提供参考依据。
方法:
2
采用临床流行病学的研究方法,以2016年1月至2018年12月于云南中医药大学第一附属医院心内科就诊的1 000例冠心病临界病变患者为研究对象,收集患者的基本信息、疾病诊断、治疗经过,以及中医望、闻、问、切所得到的症状、体征、舌象、脉象等四诊信息,以及相关临床资料,采用聚类分析及因子分析的研究方法对调查所收集的信息进行数理统计分析,并结合专家组意见进行一定探讨。
结果:
2
①聚类分析结果提示,符合冠心病临界病变临床实际的基础中医证型主要有6类:血瘀证、痰浊证、寒凝心脉证、气虚证、心肾阴虚证,心阳不振证;②在聚类分析的基础上进行因子分析,初步确定了各个基础证型的主证和次证;③由于各个证型存在重复内容或可统一组合的证型,结合专家组意见,将冠心病临界病变的中医证型总结为5类,即痰浊瘀阻证(368例,占比36.80%),寒凝心脉证(156例,占比15.60%),气虚血瘀证(315例,占比31.50%),心肾阴虚证(91例,占比9.10%),心阳不振证(70例,占比7.00%),其主证和次证参考6类基础证型的因子分析结果。
结论:
2
通过冠心病临界病变的中医证候聚类分析及因子分析,可为该病的中医证候分类研究以及证候诊断标准的建立做铺垫,具有重要临床意义。
Objective:
2
To explore the distribution characteristics of traditional Chinese medicine (TCM) syndromes of critical lesions of coronary heart disease
and determine the classification and diagnostic criteria of syndromes
so as to provide reference for the differentiation and treatment of this disease.
Method:
2
Totally 1 000 patients with critical lesions of coronary heart disease treated in the Department of Cardiology
Yunnan Provinceal Hospital of TCM from January 2016 to December 2018 were selected as the subjects by clinical epidemiological research methods. Basic information
diagnosis and treatment of the patients
as well as the information obtained through observation
hearing
inquiry and pulse-taking of TCM were collected. Symptoms
signs
tongue signs
pulse signs and other four diagnostic information
as well as relevant clinical data were collected for mathematical statistics analysis by cluster analysis and factor analysis research methods
and expert group opinions were also included in discussion.
Result:
2
First
the results of cluster analysis showed six types of basic TCM syndromes in accordance with the clinically actual critical lesions of coronary heart disease: blood stasis syndrome
phlegm turbidity syndrome
cold congealing heart pulse syndrome
Qi deficiency syndrome
heart-kidney Yin deficiency syndrome
Heart-Yang deficiency syndrome. Second
a factor analysis was carried out on the basis of cluster analysis
and the main syndromes of each basic syndromes were preliminarily determined. Third
because of the duplicate content or the unified combination of different syndromes
the TCM syndromes of the critical lesions of coronary heart disease can be summarized in five categories
namely phlegm turbidity and blood stasis syndrome (368 cases
36.80%)
cold congestion heart pulse syndrome (156 cases
15.60%)
Qi deficiency and blood stasis syndrome (315 cases
31.50%)
Yin deficiency of heart and kidney (91 cases
9.10%) and Heart-Yang depression (70 cases
7.00%). The main and secondary syndromes refer to factor analysis results of six basic syndromes.
Conclusion:
2
Cluster analysis and factor analysis can be made on TCM syndromes of critical lesions of coronary heart disease to pave the way for the classification of TCM syndromes and the establishment of diagnostic criteria of TCM syndromes of coronary heart disease
with an important clinical significance.
冠心病临界病变中医证候分布规律聚类分析因子分析
critical lesions of coronary heart diseasetraditional Chinese medicine (TCM) syndromesdistribution lawcluster analysisfactor analysis
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