摘要
目的:运用多元统计学方法分析慢性乙肝湿热中阻证与非湿热中阻证的多种指标,以期发现对慢性乙肝湿热中阻证辨证最有价值的指标并建立相关判别方程式。方法:按纳入标准选取2004-2005年泸州医学院第二医院肝病门诊慢性乙肝患者250例,分为湿热中阻证组和非湿热中阻证组,对其24项指标采用多元统计方法分析,建立湿热中阻证逐步判别方程和逐步logistic判别方程,并验证效能。结果:天门冬氨酸氨基转移酶(AST)、总胆红素(TBIL)、乙型肝炎e抗原(HBeAg)、舌质颜色、舌苔在两证间有显著性差异,是方程纳入指标;所建立两判别方程的准确度、敏感度和特异度分别为80.0%、71.4%、89.7%和84.8%、84.2%、85.5%。结论:上述5项指标在慢性乙肝湿热中阻证中医辨证中具有重要价值,所得方程式经交互验证后效能较高,具有较高的临床实用性。
Ojective: To get significant indexes of stagnation of damp-heat in middle-jiao of chronic viral hepatitis B and to establish valuable and efficient multivariant discriminant function in relation to clinical TCM differentiation of symptoms and signs about it by multi-statistical method. Methods: Choose 250 chronic viral hepatitis B patients who meet the entry criterion, from the out-patient department of specialist of hepatopathy in the second hospital affiliated to Lu Zhou Medical College during 2004-2005, and divided them into two groups, stagnation of damp-heat in middle-jiao syndrome and non-stagnation of damp-heat in middle-jiao syndrome. 24 indexes in the two syndrome were analyzed by multistatistical method such as stepwise discrimination analysis and stepwise logistic regression and discrimination analysis, to establish the discriminant function of stagnation of dampheat in middle-jiao. Results: The differences of TBIL, AST, HBeAg, tongue fur, colour of tongue were obvious on distributions of the two groups, and these discriminant functions had higher accuracy rate, sensitivity and specificity ( 80.0%, 71.4%, 89.7%and84.8%, 84.2%, 85.5% ) . Conclusion: TBIL, AST, HBeAg, tongue fur, colour of tongue have important value to the stagnation of damp-heat in middle- jiao syndrome, and these discriminant functions had higher efficacy and better clinical practicability.
出处
《中华中医药杂志》
CAS
CSCD
北大核心
2008年第12期1060-1063,共4页
China Journal of Traditional Chinese Medicine and Pharmacy
基金
四川省中医管理局应用研究科研基金课题(No.2003A28)
关键词
慢性乙型病毒性肝炎
辨证客观化
湿热中阻证
多元统计学分析
Chronic viral hepatitis B
Objective differentiation of symptoms and signs
Stagnation of damp-heat in middle-jiao syndrome
Multi-statistical analysis