Teramoto Kei, Takeda Toshihiro, Mihara Naoki, Shimai Yoshie, Manabe Shirou, Kuwata Shigeki, Kondoh Hiroshi, Matsumura Yasushi
Department of Medical Informatics, Graduate School of Medicine, Osaka University, Suita, Japan.
Division of Medical Informatics, Tottori University Hospital, Yonago, Japan.
JMIR Med Inform. 2021 Nov 1;9(11):e28763. doi: 10.2196/28763.
Medicines may cause various adverse reactions. An enormous amount of money and effort is spent investigating adverse drug events (ADEs) in clinical trials and postmarketing surveillance. Real-world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients.
In this study, we generated a patient medication history database from physician orders recorded in EMRs, which allowed the period of medication to be clearly identified.
We developed a method for detecting ADEs based on the chronological relationship between the presence of an adverse event and the medication period. To verify our method, we detected ADEs with alanine aminotransferase elevation in patients receiving aspirin, clopidogrel, and ticlopidine. The accuracy of the detection was evaluated with a chart review and by comparison with the Roussel Uclaf Causality Assessment Method (RUCAM), which is a standard method for detecting drug-induced liver injury.
The calculated rates of ADE with ALT elevation in patients receiving aspirin, clopidogrel, and ticlopidine were 3.33% (868/26,059 patients), 3.70% (188/5076 patients), and 5.69% (226/3974 patients), respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADEs were detected. Our method accurately predicted ADEs in 90% (27/30patients) treated with aspirin, 100% (9/9 patients) treated with clopidogrel, and 100% (4/4 patients) treated with ticlopidine. Only 3 ADEs that were detected by the RUCAM were not detected by our method.
These findings demonstrate that the present method is effective for detecting ADEs based on EMR data.
药物可能会引起各种不良反应。在临床试验和上市后监测中，人们花费了大量的资金和精力来调查药物不良事件（ADEs）。来自多个电子病历（EMRs）的真实世界数据能够让我们轻松了解实际患者中发生的ADEs。
在本研究中，我们从电子病历中记录的医生医嘱生成了患者用药史数据库，从而能够清晰地确定用药时间段。
我们开发了一种基于不良事件出现与用药时间段之间的时间顺序关系来检测ADEs的方法。为了验证我们的方法，我们在接受阿司匹林、氯吡格雷和噻氯匹定治疗的患者中检测了丙氨酸氨基转移酶升高的ADEs。通过病历审查并与检测药物性肝损伤的标准方法鲁塞尔·优克福因果关系评估法（RUCAM）进行比较，对检测的准确性进行了评估。
接受阿司匹林、氯吡格雷和噻氯匹定治疗的患者中，计算得出的丙氨酸氨基转移酶升高的ADEs发生率分别为3.33%（868/26059例患者）、3.70%（188/5076例患者）和5.69%（226/3974例患者），这与之前报告的发生率一致。我们审查了检测到ADEs的患者的病历。我们的方法在90%（27/30例）接受阿司匹林治疗的患者、100%（9/9例）接受氯吡格雷治疗的患者和100%（4/4例）接受噻氯匹定治疗的患者中准确预测了ADEs。鲁塞尔·优克福因果关系评估法检测到的ADEs中，只有3例未被我们的方法检测到。
这些发现表明，目前的方法对于基于电子病历数据检测ADEs是有效的。
