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基于集对分析探讨下肢动脉粥样硬化气虚痰瘀证的辨证因子
王兆政, 陈咸川, 钱风华, 姜恺, 蒲昱伶, 张嘉伟, 沈融
上海中医药大学附属岳阳中西医结合医院老年科, 上海 200437
摘要:
[目的] 初步筛选与下肢动脉粥样硬化(LEAD)气虚痰瘀证中医辨病辨证相关的联系因子,为提高LEAD气虚痰瘀证临床辨证的准确性提供参考。[方法] 总结归纳 500 例LEAD气虚痰瘀证患者的一般情况、症状体征、辅助检查结果,基于集对分析的方法,筛选与LEAD气虚痰瘀证中医辨证相关的辨证因子,并按联系数大小排序。[结果] 综合LEAD与无LEAD人群,通过势函数可得影响LEAD气虚痰瘀证的主要因子是“痰多≥1分”与“ABI<0.9”,中等因子依次为“头重如裹≥1分”“有糖尿病史”“有吸烟史”“苔白腻或黄腻≥2分”“面色淡白或萎黄≥2 分”“男性”“唇色紫暗≥1分”“有高血压病史”“神倦乏力≥2分”“胸闷脘痞≥2分”“心悸≥1 分”“年龄≥60 岁”,余均为次要因子;研究LEAD气虚痰瘀证的中医辨证因子,通过三元联系数发现“舌淡或紫暗或有瘀斑瘀点,或有齿印”“神倦乏力”“脉弦滑或涩、结代、或细弱”“苔白腻或黄腻”“面色淡白或萎黄”为LEAD气虚痰瘀证的主要辨证因子;基于四元联系数的三阶偏联系数,发现“头晕目眩”“神倦乏力”“痰多”“唇色紫暗”“脉弦滑或涩、结代、或细弱”“苔白腻或黄腻”6个辨证因子总体有向严重程度重的潜在发展趋势,而其余辨证因子有向严重程度轻的潜在发展趋势。[结论] 通过集对分析法得到LEAD气虚痰瘀证的主要辨证因子与次要辨证因子符合临床实际,有助于构建LEAD的辨证模型,有助于中医辨证的客观化,从而提高临床辨证的准确性,同时有利于临床对部分危险因素及辨证因子进行提早干预,提高临床疗效。
关键词:  集对分析  下肢动脉粥样硬化  气虚痰瘀证  辨证因子  偏联系数
DOI:10.11656/j.issn.1672-1519.2025.02.03
分类号:R543.5
基金项目:上海市进一步加快中医药事业发展三年行动计划项目:中医优势病种培育建设项目[ZY(2018-2020)-ZYBZ-13]。
Discussion on syndrome differentiation factors of lower extremity atherosclerosis disease with qi deficiency and phlegm stasis syndrome based on set pair analysis
WANG Zhaozheng, CHEN Xianchuan, QIAN Fenghua, JIANG Kai, PU Yuling, ZHANG Jiawei, SHEN Rong
Department of Geriatrics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
Abstract:
[Objective] To preliminarily identify the associated factors of traditional Chinese medicine(TCM) syndrome differentiation of qi deficiency and phlegm stasis in lower extremity atherosclerosis disease(LEAD),to enhance the precision of clinical syndrome differentiation for this condition. [Methods] The general condition,symptoms,signs,and auxiliary examination results of 500 patients diagnosed with qi deficiency and phlegm stasis syndrome of LEAD were summarized. Based on the set pair analysis method,the factors associated with TCM syndrome differentiation of qi deficiency and phlegm stasis syndrome of LEAD were screened and sorted according to the connection number. [Results] According to the potential function,the main factors affecting the qi deficiency and phlegm stasis syndrome in patients with and without LEAD were “phlegm ≥1 point” and “ABI <0.9”,while the medium factors were “head weight such as coating ≥1 point”“diabetes history”“smoking history”“white or yellow greasy fur ≥2 points”“pale or yellow complexion ≥2 points”“male gender”“lip color purple and dark≥1 point”“history of hypertension”“spirit fatigue ≥2 points”“chest tightness ≥2 points”“palpitation ≥1 point”“age ≥60 years old”,and the rest were considered minor factors. To study the TCM syndrome differentiation of LEAD with qi deficiency and phlegm stasis syndrome,it was found that “pale or dark tongue,or petechiae,or tooth marks” “spiritual fatigue” “smooth or astringent pulse string,knot,or weak” “white or yellow greasy fur” “pale or yellow complexion” were the main TCM syndrome differentiation factors. Based on the three-order partial connection number of the four-element connection number,it was found that the six differentiation factors of “dizziness” “spiritual fatigue” “phlegm” “lip color purple and dark” “smooth or astringent pulse string,knot,or weak” “white or yellow greasy fur” showed a potential development trend towards heavier degrees of severity,while the other differentiation factors tended to be milder. [Conclusion] The main and minor syndrome differentiation factors of qi deficiency phlegm stasis syndrome of LEAD,screened and sorted by the set pair analysis method,were in line with clinical practice,which is conducive to the construction of syndrome differentiation model of LEAD and help to realize the objectification of TCM syndrome differentiation,thus improving the accuracy of clinical syndrome differentiation,and at the same time,it is beneficial to early clinical intervention of some risk factors and syndrome differentiation factors,to improve clinical therapeutic efficacy.
Key words:  set pair analysis  lower extremity atherosclerosis disease  qi deficiency and phlegm stasis syndrome  syndrome differentiation factor  partial connection coefficient
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