School of Population Health, The University of Western Australia, Perth, Australia.
Pharmacoepidemiol Drug Saf. 2013 Nov;22(11):1159-70. doi: 10.1002/pds.3469. Epub 2013 Jun 25.
To use a case-time-control design to derive preliminary estimates of unplanned hospitalisations attributable to suspected high-risk medications in elderly Western Australians.
Using pharmaceutical claims linked to inpatient and other health records, the study applied a case-time-control design and conditional logistic regression to estimate odds ratios (ORs) for unplanned hospital admissions associated with anticoagulants, antirheumatics, opioids, corticosteroids and four major groups of cardiovascular drugs. Attributable fractions (AFs) were derived from the ORs to estimate the number and proportion of admissions associated with drug exposure. Results were compared with those obtained from a more conventional method using International Classification of Diseases (ICD) external cause codes to identify admissions related to adverse drug events.
The study involved 1 899 699 index hospital admissions. Six of the eight drug groups were associated with an increased risk of unplanned hospitalisation, opioids (adjusted OR = 1.81, 95%CI 1.75-1.88; AF = 44.9%) and corticosteroids (1.48, 1.42-1.54; 32.2%) linked with the highest risks. For all six, the estimated number of hospitalisations attributed to the medication in the exposed was higher (two to 31-fold) when derived from the case-time-control design compared with identification from ICD codes.
This study provides an alternative approach for identifying potentially harmful medications and suggests that the use of ICD external causes may underestimate adverse drug events. It takes drug exposure into account, can be applied to individual medications and may overcome under-reporting issues associated with conventional methods. The approach shows great potential as part of a post-marketing pharmacovigilance monitoring system in Australia and elsewhere.
使用病例时间对照设计,在西澳大利亚的老年人群中初步估计疑似高危药物导致的非计划性住院。
通过药物使用记录与住院和其他健康记录的关联,本研究采用病例时间对照设计和条件逻辑回归,估计与抗凝药、抗风湿药、阿片类药物、皮质类固醇和四大类心血管药物相关的非计划性住院的入院比值比(OR)。从 OR 中推导出归因分数(AF),以估计与药物暴露相关的入院人数和比例。将结果与使用国际疾病分类(ICD)外部原因代码识别与药物不良事件相关的入院的更传统方法的结果进行比较。
该研究共涉及 1899699 例指数住院。在所研究的 8 个药物组中,有 6 个与非计划性住院风险增加相关,阿片类药物(调整后的 OR=1.81,95%CI 1.75-1.88;AF=44.9%)和皮质类固醇(1.48,1.42-1.54;32.2%)风险最高。对于所有 6 个药物组,与从 ICD 编码中识别相比,从病例时间对照设计中得出的暴露药物导致的住院人数估计更高(2 到 31 倍)。
本研究提供了一种识别潜在有害药物的替代方法,并表明使用 ICD 外部原因可能低估药物不良事件。它考虑了药物暴露,可以应用于单一药物,并可能克服与传统方法相关的报告不足问题。该方法作为澳大利亚和其他地方上市后药物监测系统的一部分具有很大的潜力。