Hoppe Christian, Obermeier Patrick, Muehlhans Susann, Alchikh Maren, Seeber Lea, Tief Franziska, Karsch Katharina, Chen Xi, Boettcher Sindy, Diedrich Sabine, Conrad Tim, Kisler Bron, Rath Barbara
Department of Pediatrics, Charité University Medical Center Berlin, Berlin, Germany.
Vienna Vaccine Safety Initiative, Berlin, Germany.
Drug Saf. 2016 Oct;39(10):977-88. doi: 10.1007/s40264-016-0437-6.
Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium.
The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases , Tenth Revision (ICD-10) codes assigned in the emergency department (ED).
From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 % of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE.
Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements.
监管机构经常收到结构不佳的安全报告，需要付出巨大努力对潜在不良事件进行事后调查。自动化问答系统可能有助于提高传递给药物警戒机构的安全信息的整体质量。本文探讨了VACC-Tool（ViVI自动病例分类工具）2.0的使用，这是一款移动应用程序，使医生能够根据14个预先定义的神经炎性不良事件（NIAE）病例定义对临床病例进行分类，并完全符合临床数据交换标准协会发布的数据标准。
VACC-Tool 2.0（测试版）的验证是在与德国柏林的罗伯特·科赫研究所合作开展的针对疑似NIAE儿童的独特质量管理项目背景下进行的。VACC-Tool用于即时病例分类以及住院期间的纵向随访。将结果与急诊科（ED）分配的国际疾病分类第十版（ICD-10）编码进行比较。
2013年7月至2014年10月，急诊科共接诊34368例患者，其中5243例住院；其中243例因疑似NIAE入院（平均年龄：8.5岁），从而参与了质量管理项目。在急诊科使用VACC-Tool成功分类了209例病例，其中69%在ED报告中被遗漏或编码错误。使用VACC-Tool进行纵向随访发现了更多的NIAE。
移动应用程序将数据标准应用到了医疗点，使临床医生能够在急诊科环境和住院随访期间确定潜在不良事件。符合临床数据交换标准协会（CDISC）的数据标准有助于根据监管要求实现数据互操作性。
