摘要
目的:探索清开灵注射剂不良反应的流行病学特点。方法:系统检索1979~2015年期刊文献,采集清开灵注射剂不良反应详细个例报道文献,在此基础上应用Microsoft ACCESS构建数据库,进而以SPSS 20.0、Clementine12.0为数据挖掘平台,综合应用贝叶斯网络、聚类分析、神经网络、关联规则Apriori算法和决策树(CART)算法开展数据挖掘研究。结果:共搜索符合纳入条件的清开灵注射剂不良反应详细个案355例。贝叶斯网络联合逻辑回归分析显示,与不良反应不同诊断结果相关的因素是用药剂量、联合用药情况和原发疾病。聚类分析、神经网络分析显示,与不良反应诊断结果相关的因素包括不良反应发生时间、年龄段、性别、配液浓度、配液用量和配液种类。关联规则Apriori算法中支持度最高的两条规则是"原发疾病类=上呼吸道感染and配液种类(名称)=葡萄糖"与"不良反应临床表现=过敏性休克"同时出现的案例占纳入ADR案例的35.71%和"原发疾病类=上呼吸道感染and配液种类(名称)=葡萄糖and给药途径(方式)=静脉滴注"与"不良反应临床表现=过敏性休克"同时出现的案例占纳入ADR案例的35.71%。结论:清开灵注射剂不良反应发生是在临床用药因素、患者体质因素和药品自身因素共同作用下的复杂表现,其发生规律尚需更大样本量的数据分析验证。
Objective: To explore the epidemiological characteristics of Qingkailing injection adverse reactions. Methods:Analysis the collected detail Qingkailing injection case reports from 1979 to 2015, apply Microsoft ACCESS data- base platform and Clementine 12. 0 and SPSS 20. 0 platform for data mining which include decision tree algorithm (CART), Bayesian networks method, clustering analysis, neural network, association rules Apriori algorithm. Results: 355 adverse reactions induced by Qingkailing injection were collected. Bayesian network joint logistic regression analysis showed that factors associated with the result of adverse drug reaction (ADR) was dosage, drug combination, primary dis- ease. Clustering analysis and neural network analysis showed that factors associated with ADR~ result includes ADR peri- od, age, gender, liquor concentration, dosage of liquor and liquor types. The outcome of association rules showed that the two rules with the highest support were"primary disease = upper respiratory tract infection and liquor types = glucose" and "adverse clinical = anaphylactic shock" cases accounted for 35.71% ; and "primary disease = the upper respiratory tract in- fection and liquor type = glccose and delivery way = intravenous drip" and "adverse clinical = auaphylactic shock" cases ac- counted for 35.71%. Conclusion: Occurrence of Qingkailing injection' s ADR may be related to such factors as clinical drug factors, physical factors and drug themselves which complex under the action of performance still needs a larger sample size analysis of the data validation.
出处
《药物流行病学杂志》
CAS
2017年第6期389-394,共6页
Chinese Journal of Pharmacoepidemiology
基金
国家自然科学基金项目(编号:81473547
81673829)
国家食品药品监督管理总局药品评价中心委托课题[编号:2016(4)001]