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多囊卵巢综合征患者超说明书用药循证医学分析

Evidence-based Medicine Analysis of Off-label Use in Patients with Polycystic Ovary Syndrome
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摘要 目的了解多囊卵巢综合征(PCOS)患者超说明书用药现状,进一步规范医院的超说明书用药管理。方法通过医院合理用药系统收集厦门大学附属妇女儿童医院2020年1—6月诊断"多囊卵巢综合征"的处方,按标准进行超说明书用药的汇总统计和循证医学分析。结果门诊多囊卵巢综合征患者超说明书用药处方共611张,超说明书发生率为19.86%,超说明书的药品品种5个,超说明书的类型以超适应证和超用法用量为主。对超说明书用药的品种进行循证分析,结果表明每个品种有循证证据支持,但证据质量等级有差异。结论通过超说明书用药的循证研究和证据质量评价,为临床的超说明书用药行为提供科学的依据和参考价值,且有助于提高医院的超说明书用药管理水平。 Objective To understand the status quo of off-label use in patients with polcystic ovary syndrome and further standardize the management of off-label use in hospitals. Methods The prescriptions of polcystic ovary syndrome(PCOS)diagnosed in Affiliated Women and Children’s Hospital of Xiamen University from January to June 2020 were collected through the information system, and the superprescriptive drugs were collected and counted and evidence-based medicine analyzed according to the standard. Results There were 611 off-label use prescriptions for polcystic ovary syndrome outpatients, the incidence of which was 19.86%. There were 5 kinds of off-label drugs. The types of off-label use prescriptions were mainly over-indications and over-usages. The results of evidence-based analysis of types of off-label drugs showed that there was evidence-based support for each variety, but the quality level of evidence was different. Conclusion Through the evidence-based research and evidence quality evaluation of the off-label drug use, it can provide scientific basis and reference value for the clinical behavior of off-label drug use, and help to improve the management level of off-label drug use in hospitals.
作者 吴雯 杨剑辉 WU Wen;YANG Jian-Hui(Affiliated Women and Children’s Hospital of Xiamen University/Department of Pharmacy,Xiamen Maternity and Child Health Hospital,Xiamen 361000,China)
出处 《中国药物经济学》 2021年第10期40-42,46,共4页 China Journal of Pharmaceutical Economics
基金 中国药学会全国医药经济信息网2018年重点项目(CMEI2018KPYJ(ZAMM)00105)。
关键词 多囊卵巢综合征 超说明书用药 药品 循证分析 证据质量 推荐意见 Polycystic ovarian syndrome Off-label use Drug Evidence-based analysis The quality of evidence Recommendations
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