Department of Paediatric and Adolescent Medicine, University Hospital Erlangen, Erlangen, Germany.
Br J Clin Pharmacol. 2013 Sep;76 Suppl 1(Suppl 1):69-77. doi: 10.1111/bcp.12127.
Computer-assisted signal generation is an important issue for the prevention of adverse drug reactions (ADRs). However, due to poor standardization of patients' medical data and a lack of computable medical drug knowledge the specificity of computerized decision support systems for early ADR detection is too low and thus those systems are not yet implemented in daily clinical practice. We report on a method to formalize knowledge about ADRs based on the Summary of Product Characteristics (SmPCs) and linking them with structured patient data to generate safety signals automatically and with high sensitivity and specificity.
A computable ADR knowledge base (ADR-KB) that inherently contains standardized concepts for ADRs (WHO-ART), drugs (ATC) and laboratory test results (LOINC) was built. The system was evaluated in study populations of paediatric and internal medicine inpatients.
A total of 262 different ADR concepts related to laboratory findings were linked to 212 LOINC terms. The ADR knowledge base was retrospectively applied to a study population of 970 admissions (474 internal and 496 paediatric patients), who underwent intensive ADR surveillance. The specificity increased from 7% without ADR-KB up to 73% in internal patients and from 19.6% up to 91% in paediatric inpatients, respectively.
This study shows that contextual linkage of patients' medication data with laboratory test results is a useful and reasonable instrument for computer-assisted ADR detection and a valuable step towards a systematic drug safety process. The system enables automated detection of ADRs during clinical practice with a quality close to intensive chart review.
计算机辅助信号生成是预防药物不良反应（ADR）的一个重要问题。然而，由于患者医疗数据的标准化程度较差，以及缺乏可计算的药物知识，用于早期 ADR 检测的计算机化决策支持系统的特异性太低，因此这些系统尚未在日常临床实践中实施。我们报告了一种基于产品特性摘要（SmPC）将 ADR 知识形式化的方法，并将其与结构化患者数据链接，以自动生成具有高灵敏度和特异性的安全信号。
构建了一个可计算的 ADR 知识库（ADR-KB），其中包含 ADR（WHO-ART）、药物（ATC）和实验室检验结果（LOINC）的标准化概念。该系统在儿科和内科住院患者的研究人群中进行了评估。
共链接了 262 个与实验室发现相关的不同 ADR 概念到 212 个 LOINC 术语。ADR 知识库被 retrospective 应用于 970 例住院患者（474 例内科患者和 496 例儿科患者）的研究人群中，这些患者接受了强化 ADR 监测。特异性从无 ADR-KB 时的 7%增加到内科患者的 73%，从儿科患者的 19.6%增加到 91%。
这项研究表明，将患者的用药数据与实验室检验结果进行上下文链接是计算机辅助 ADR 检测的一种有用且合理的工具，也是迈向系统性药物安全流程的有价值的一步。该系统可在临床实践中实现 ADR 的自动检测，其质量接近密集图表审查。
