来源：风湿病与关节炎,2023,12（4）:5-9.

黄文政教授治疗高尿酸血症用药规律研究及学术思想探析（临床研究）

祝昌昊^1,2，郭　望^1,2，张　婧^1,2，王钰涵^1,2，邸嘉玮^1,2，王耀光^1,2

　　【摘　要】目的：基于数据挖掘方法探讨首届全国名中医黄文政教授治疗高尿酸血症的临床遣方用药规律。方法：收集2019年1月至2021年12月黄文政教授门诊治疗高尿酸血症的处方，应用Microsoft Excel 2019、SPSS 23.0及SPSS Modeler 18进行中药使用频数、性味、归经、关联规则及聚类分析。结果：最终纳入116个处方进行统计分析，涉及中药200味，其中频次较高的中药以泄浊化瘀药和调理脾肾药为主。基于关联规则分析得到关联最为紧密的药对为萆薢-土茯苓，聚类分析得到4个核心药物组合。结论：黄文政教授重视高尿酸血症本虚标实的病机本质，以补肾泄浊、健脾活血、疏风通痹为主要治则治法，用药随症变化，标本兼顾，临床疗效显著。

　　【关键词】　高尿酸血症；痹证；数据挖掘；用药规律；黄文政

Study on Professor HUANG Wen-zheng’s Medication Rules in the Treatment of Hyperuricemia and His Academic Thought

ZHU Chang-hao,GUO Wang,ZHANG Jing,WANG Yu-han,DI Jia-wei,WANG Yao-guang

　　【ABSTRACT】Objective:Based on data mining methods,to explore professor HUANG Wen-zheng's clinical prescription and medication rules in the treatment of hyperuricemia.Methods:Professor HUANG Wen-zheng’s prescriptions of treating hyperuricemia from January 2019 to December 2021 were collected.Microsoft Excel 2019,SPSS 23.0 and SPSS Modeler 18 were used to analyze the frequency of use,nature,flavor,channel entry,association rules and cluster analysis of Chinese medicine.Results:One hundred and sixteen prescriptions were finally included for statistical analysis,involving 200 Chinese medicines,of which the most frequent Chinese medicines were those that can relieve turbid and blood stasis and regulate spleen and kidney.Based on the analysis of association rules,the most closely related medicinal pair was Bixie（Rhizoma Dioscoreae Hypoglaucae）and Tufuling（Rhizoma Smilacis Glabrae）,and 4 core medicinal combinations were obtained by cluster analysis.Conclusion:Professor HUANG Wen-zheng attaches great importance to the pathogenesis of hyperuricemia,taking "tonifying the kidney to remove turbidity,invigorating the spleen and activating blood circulation,dredging the wind and clearing the bi" as the main treatment principle and method.The medication changes with the syndrome and both tip and root are taken into account,whose clinical effect is significant.

　　【Keywords】 hyperuricemia;bi;data mining;medication law;HUANG Wen-zheng