基于TURBO模型、风险矩阵法和Borda序值法识别某院血必净注射液不良反应的高风险人群
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摘要
目的利用TURBO模型、风险矩阵法和Borda序值法,筛选出发生不良反应的高风险人群。方法选取某院2015～2017年上报国家药物不良反应监测中心25例血必净注射液不良反应,以患者年龄和性别作为特征,分为A-L共12个组,将不良反应发生率和不良反应严重程度指数作为风险评价要素,运用风险矩阵和Borda序值法,将12个组按照风险高低进行排序,得到高风险人群。结果特征人群组风险大小排序为:J> B> L> G> K> C> E> A> H> D> F> I,"51～60岁女性"风险最高,其次为"31～40岁男性"。结论利用TURBO模型、风险矩阵法和Borda序值法,可以鲜明而直观的表达风险高低,该方法也可运用到其他类型药物不良反应的风险评价中。
Objective To screen high-risk population with adverse reactions of xuebijing injection by using TURBO model, risk matrix method and Borda ordinal value method. Methods Twenty-five cases of adverse drug reactions of Xuebijing injection reported to the National Adverse Drug Reaction Monitoring Center in our hospital from 2015 to 2017 were divided into 12 groups with age and sex as characteristics. We taked the incidence of adverse reactions and the severity index of adverse reactions as risk assessment factors, using the risk matrix and Borda ordinal value method to find out the high-risk population. Results The risk ranking of the characteristic population group was J > B > L > G > K > C > E > A > H > D > F > I. The risk of "51 ～ 60 year old female" was the highest, followed by "31 ～ 40 year old male". Conclution Using TURBO model, risk matrix method and Borda ordinal value method, we can clearly and intuitively express the level of risk. This method can also be applied to the risk assessment of adverse reactions of other types of drugs.
Objective To screen high-risk population with adverse reactions of xuebijing injection by using TURBO model, risk matrix method and Borda ordinal value method. Methods Twenty-five cases of adverse drug reactions of Xuebijing injection reported to the National Adverse Drug Reaction Monitoring Center in our hospital from 2015 to 2017 were divided into 12 groups with age and sex as characteristics. We taked the incidence of adverse reactions and the severity index of adverse reactions as risk assessment factors, using the risk matrix and Borda ordinal value method to find out the high-risk population. Results The risk ranking of the characteristic population group was J > B > L > G > K > C > E > A > H > D > F > I. The risk of "51 ～ 60 year old female" was the highest, followed by "31 ～ 40 year old male". Conclution Using TURBO model, risk matrix method and Borda ordinal value method, we can clearly and intuitively express the level of risk. This method can also be applied to the risk assessment of adverse reactions of other types of drugs.
引文
[1]孔敏,许惠琴. 2013-2015年泰州地区中药注射液不良反应调查分析[J].黑龙江医药,2015,28(6):1285-1288.
[2]陈瑞家. 119例血必净注射液不良反应的文献分析[J].海峡药学,2016,28(12):290-291.
[3]王兰明.我国药品不良反应监测工作的进展与展望[J].中国药业,2003,12(1):31-32.
[4]吴长青,朱文涛.基于风险矩阵等方法的药品不良反应风险评价的探讨[J].中国中医药现代远程教育,2010,8(13):20-22.
[5]杨云,孙宏,康正,等.卫生应急风险矩阵法与Borda序值法评估[J].中国公共卫生,2016,32(6):815-817.
[6]郑锐.用Borda法对风险进行等级排序[J].企业改革与管理,2015,(11):154.
[7]崔媛,樊欣,郭冬杰,等.中日友好医院门诊高风险用药人群识别与分析[J].临床药物治疗杂志,2015,13(02),33-37.
[2]陈瑞家. 119例血必净注射液不良反应的文献分析[J].海峡药学,2016,28(12):290-291.
[3]王兰明.我国药品不良反应监测工作的进展与展望[J].中国药业,2003,12(1):31-32.
[4]吴长青,朱文涛.基于风险矩阵等方法的药品不良反应风险评价的探讨[J].中国中医药现代远程教育,2010,8(13):20-22.
[5]杨云,孙宏,康正,等.卫生应急风险矩阵法与Borda序值法评估[J].中国公共卫生,2016,32(6):815-817.
[6]郑锐.用Borda法对风险进行等级排序[J].企业改革与管理,2015,(11):154.
[7]崔媛,樊欣,郭冬杰,等.中日友好医院门诊高风险用药人群识别与分析[J].临床药物治疗杂志,2015,13(02),33-37.