Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.
Department of Signal Processing Systems, Faculty of Electronic Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Clin Pharmacol Ther. 2022 Aug;112(2):382-390. doi: 10.1002/cpt.2624. Epub 2022 Jun 27.
Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.
药物-药物相互作用(DDI)经常引发药物不良事件或降低疗效。然而,由于与特定患者无关,大多数 DDI 警报都被忽略了。基本的 DDI 临床决策支持(CDS)系统提供了减少无关 DDI 警报数量而不遗漏相关警报的可能性有限。设计了计算机化决策树规则来根据上下文抑制无关的 DDI 警报。进行了一项交叉研究,以比较住院患者中上下文相关和基本 DDI 管理的临床实用性。首先,在临床实践中使用基本的 DDI-CDS 系统,同时在后台收集上下文相关的 DDI 警报。接下来,这个过程被颠倒了。所有至少有一个 DDI 警报的住院患者的药物医嘱(MO)都包括在内。使用以下指标来评估临床实用性:阳性预测值(PPV)、阴性预测值(NPV)、每 1000 个 MO 的药剂干预次数(PI)和每 1000 个 MO 的 DDI 管理时间中位数。在基本的 DDI 管理阶段 1,每天纳入 1919 个 MO,触发 220 个 DDI 警报/1000 MO;向药剂科工作人员展示 57 个基本的 DDI 警报/1000 MO;PPV 为 2.8%,每 1000 MO 的 PI 为 1.6 个,成本为 37.2 分钟/1000 MO。上下文相关的 CDS 系统没有遗漏任何 DDI(NPV 为 100%)。在上下文相关的 DDI 管理阶段 1,每天纳入 1853 个 MO,触发 244 个基本 DDI 警报/1000 MO,向药剂科工作人员展示 9.6 个上下文相关的 DDI/1000 MO;PPV 为 41.4%(P < 0.01),每 1000 MO 的 PI 为 4.0 个(P < 0.01),每 1000 MO 的时间为 13.7 分钟。上下文相关的 DDI 管理的临床实用性超过了基本的 DDI 管理。