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Methods for retrospective detection of drug safety signals and adverse events in electronic general practice records

机译:回顾性检测电子全科医学记录中药物安全信号和不良事件的方法

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Background: Examination of clinical data routinely recorded in general practice provides significant opportunities for identifying and quantifying medicine-related adverse events not captured by spontaneous adverse reaction reporting systems. Robust pharmacovigilance methods for detecting and monitoring adverse events due to treatment with new and existing medicines are required to estimate the true extent of adverse events experienced by primary care patients. Objectives: The aim of the study was to examine evidence of adverse events contained in general practice electronic records and to study observed events related to selective serotonin reuptake inhibitors (SSRIs) as an example of drug-specific pharmaceutical surveillance achievable with these data. Methods: Electronic clinical records for a cohort of 338 931 patients consulting from 2002 to 2007 were extracted from the patient management systems of 30 primary care clinics in New Zealand. Medical warnings files, prescription records and free text consultation notes were used to identify physician-recorded treatment cautions, including adverse events and medicines they were associated with. A structured chronological analysis of prescriptions, consultation notes and adverse events relating to patients prescribed the SSRI citalopram was undertaken, and included investigating reasons for switching treatment to another SSRI (fluoxetine or paroxetine) as a method for detecting evidence of drug safety signals. We compared the number of adverse events identified for patients at one practice with the number spontaneously reported to New Zealand's Centre for Adverse Reactions Monitoring (CARM). Results: During the 6-year study period, 173 478 patients received 4 811 561 prescriptions. There were 37 397 allergies, adverse events and other warnings recorded for 24 994 patients (7.4%); adverse events relating to 65 different types of drug were reported. Medicines most frequently implicated in adverse event reports were antibacterials, analgesics, antihypertensive medicines, lipid-modifying agents and skin preparations. Citalopram was prescribed for 5612 patients, and 701 adverse events relating to citalopram were identified in the electronic health records of 473 (8.4%) patients. A total of 713 (12.7%) patients changed treatment from citalopram to another SSRI, and 164 reasons for the switch were identified: suspected adverse drug effects for 129 (78.7%), lack of effect for 29 (17.7%) and patient preference for 6 (3.7%). The most common adverse events preceding the switch were anxiety, nausea and headaches. Of the 725 adverse events and medical warnings recorded at one practice, 21 (2.9%) were spontaneously reported to the CARM. Conclusions: Routinely recorded general practice data provide a wealth of opportunities for monitoring drug safety signals and for other patient safety issues. Medical warning records and consultation notes contain a wealth of information on adverse events but structured search methodologies are often required to identify these.
机译:背景:检查常规记录的常规临床数据为识别和量化未被自发不良反应报告系统捕获的与药物相关的不良事件提供了重要机会。需要可靠的药物警戒方法来检测和监测由于使用新药和现有药物治疗而引起的不良事件,以估计初级保健患者经历的不良事件的真实程度。目的:该研究的目的是检查一般实践电子记录中包含的不良事件的证据,并研究与选择性5-羟色胺再摄取抑制剂(SSRIs)相关的观察到的事件,作为可通过这些数据实现的药物特异性药物监测的实例。方法:从新西兰30所初级保健诊所的患者管理系统中提取了2002年至2007年接受咨询的338 931名患者的电子临床记录。医疗警告文件,处方记录和自由文本咨询笔记用于识别医师记录的治疗注意事项,包括不良事件和与之相关的药物。对处方SSRI西酞普兰的患者的处方,咨询笔记和不良事件进行了结构化的时间顺序分析,其中包括调查将治疗切换为另一种SSRI(氟西汀或帕罗西汀)的原因,作为检测药物安全信号证据的方法。我们将一种实践中为患者确定的不良事件数量与自发报告给新西兰不良反应监测中心(CARM)的数量进行了比较。结果:在为期6年的研究期内,有173478例患者接受了4 811 561张处方。 24 994例患者中有37 397例过敏,不良事件和其他警告记录(7.4%);报告了与65种不同类型药物有关的不良事件。不良事件报告中最常涉及的药物是抗菌药,止痛药,降压药,脂质修饰剂和皮肤制剂。已向5612名患者开出了西酞普兰的处方药,在473例患者(8.4%)的电子健康记录中确定了701例与西酞普兰有关的不良事件。共有713名(12.7%)患者将治疗从西酞普兰改为另一种SSRI,并确定了164种转换原因:疑似药物不良反应129例(78.7%),无效29例(17.7%)以及患者首选6(3.7%)。转换之前最常见的不良事件是焦虑,恶心和头痛。在一项实践中记录的725例不良事件和医疗警告中,有21例(2.9%)自发报告给CARM。结论:常规记录的普通执业数据为监测药物安全信号和其他患者安全问题提供了大量机会。医疗警告记录和咨询记录包含有关不良事件的大量信息,但通常需要结构化的搜索方法来识别这些情况。

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