Paix A D, Bullock M F, Runciman W B, Williamson J A
Princess Royal University Hospital, Orpington, Kent, UK.
Qual Saf Health Care. 2005 Jun;14(3):e15. doi: 10.1136/qshc.2002.004119.
Modern anaesthetic practice relies upon the administration of a wide range of potent drugs given by a variety of routes, at times in haste or under conditions of stress. Problems associated with drug administration make up the largest group of incidents reported during anaesthesia, with outcomes including major morbidity and death. It was decided to examine the role of a structured approach to the diagnosis and management of drug problems under anaesthesia.
To examine the role of a previously described core algorithm "COVER ABCD-A SWIFT CHECK", supplemented by a specific sub-algorithm for drug problems, in the detection and management of drug problems occurring in association with anaesthesia.
The potential performance of this structured approach for the relevant incidents among the first 4000 incidents reported to the Australian Incident Monitoring Study (AIMS) was compared with the actual performances as reported by the anaesthetists involved.
Among the first 4000 reports received by AIMS there were 1199 reports which detailed 1361 incidents involving the use of drugs. Contributing factors named included errors of judgement (20%), lack of attention (17%), and drugs deemed to have been given in haste. Major morbidity or prolonged stay ensued in over one quarter of reports and 15 patients (1.25%) died. Drug overdose, side effects, and allergic reactions accounted for the majority of serious outcomes.
It was judged that the use of the COVER-ABCD algorithm during the course of an anaesthetic, properly applied, would prevent many drug related incidents from occurring. The sub-algorithm presented here provides a systematic framework for detecting the causes of drug related incidents.
现代麻醉实践依赖于通过多种途径给予各种强效药物，有时是在匆忙或压力条件下进行。与药物给药相关的问题构成了麻醉期间报告的最大一类事件，其后果包括严重的发病和死亡。因此决定研究一种结构化方法在麻醉期间药物问题诊断和管理中的作用。
研究先前描述的核心算法“COVER ABCD - A SWIFT CHECK”（辅以针对药物问题的特定子算法）在检测和管理与麻醉相关的药物问题中的作用。
将这种结构化方法对向澳大利亚事件监测研究（AIMS）报告的前4000起事件中相关事件的潜在表现，与参与的麻醉医生报告的实际表现进行比较。
在AIMS收到的前4000份报告中，有1199份报告详细描述了1361起涉及药物使用的事件。所提及的促成因素包括判断失误（20%）、注意力不集中（17%）以及被认为给药匆忙。超过四分之一的报告出现了严重发病或住院时间延长的情况，15名患者（1.25%）死亡。药物过量、副作用和过敏反应占严重后果的大部分。
据判断，在麻醉过程中正确应用COVER - ABCD算法将防止许多与药物相关的事件发生。此处提出的子算法为检测与药物相关事件的原因提供了一个系统框架。
