Zhao Ying, Lu Haidong, Thai Sydney, Li Xiaotong, Hui John, Tang Huilin, Zhai Suodi, Sun Lulu, Wang Tiansheng
Department of Pharmacy, Peking University Third Hospital, Beijing, China.
Department of Pharmacy, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Int J Clin Pharm. 2018 Aug;40(4):862-869. doi: 10.1007/s11096-018-0594-z. Epub 2018 Feb 20.
Background Pharmacovigilance databases are utilized to identify serious adverse drug events (ADEs). In China, very few studies have evaluated the validity of using pharmacovigilance databases to identify drug-induced anaphylaxis (DIA). Objective We aimed to develop and validate an algorithm to identify DIA using the Beijing Pharmacovigilance Database (BPD). Setting ADEs from the BPD mainly spontaneously reported from 94 hospitals in Beijing, China. Method Using the diagnoses, we developed an algorithm to identify potential DIAs from the BPD between January 2004 and December 2014. A sample of 500 patients was randomly selected for chart abstraction. Two physician adjudicators assessed whether DIA occurred using the published clinical criteria as the gold standard. Main outcome measure Positive predictive values (PPVs) and 95% confidence intervals of the algorithm and algorithm criteria components were calculated. Results 500 patients (53.2% female; the mean age 48.2 years) with potential DIA were selected using the algorithm. 444 were adjudicated as having anaphylaxis by physicians. The PPV of the overall algorithm was 88.8% (95% CI 86.0-91.6%). PPV for the algorithm only using specific diagnoses of "anaphylactic shock", "anaphylactic reaction", and "anaphylactoid reaction [severe]" was 89.6% (95% CI 86.6-92.4%); this partial algorithm identified 387 (87.2%) DIAs. The diagnosis that identified the most DIAs (83.8%) was "anaphylactic shock", with a PPV of 91.6% (95% CI 88.9-94.3%). Conclusion The overall algorithm identified a greater number of DIAs than the algorithm that only used specific diagnoses; however, its PPV was slightly lower. We were able to identify DIAs with the algorithm we developed.
背景 药物警戒数据库用于识别严重药物不良事件（ADEs）。在中国，很少有研究评估使用药物警戒数据库识别药物性过敏反应（DIA）的有效性。目的 我们旨在开发并验证一种使用北京药物警戒数据库（BPD）识别DIA的算法。设置 BPD中的ADEs主要来自中国北京94家医院的自发报告。方法 利用诊断信息，我们开发了一种算法，以识别2004年1月至2014年12月期间BPD中的潜在DIA。随机抽取500例患者进行病历摘要。两名医生评审员以已发表的临床标准作为金标准，评估是否发生了DIA。主要结局指标 计算该算法及算法标准组成部分的阳性预测值（PPV）和95%置信区间。结果 使用该算法选择了500例潜在DIA患者（女性占53.2%；平均年龄48.2岁）。444例被医生判定为过敏反应。总体算法的PPV为88.8%（95%CI 86.0-91.6%）。仅使用“过敏性休克”、“过敏反应”和“类过敏反应[严重]”特定诊断的算法的PPV为89.6%（95%CI 86.6-92.4%）；该部分算法识别出387例（87.2%）DIA。识别出最多DIA（83.8%）的诊断是“过敏性休克”，PPV为91.6%（95%CI 88.9-94.3%）。结论 总体算法识别出的DIA比仅使用特定诊断的算法更多；然而，其PPV略低。我们能够使用我们开发的算法识别DIA。
