Morris R W, Watterson L M, Westhorpe R N, Webb R K
Sydney Medical Simulation Centre, Royal North Shore Hospital, St Leonards, New South Wales, Australia.
Qual Saf Health Care. 2005 Jun;14(3):e11. doi: 10.1136/qshc.2002.004440.
Hypotension is commonly encountered in association with anaesthesia and surgery. Uncorrected and sustained it puts the brain, heart, kidneys, and the fetus in pregnancy at risk of permanent or even fatal damage. Its recognition and correction is time critical, especially in patients with pre-existing disease that compromises organ perfusion.
To examine the role of a previously described core algorithm "COVER ABCD-A SWIFT CHECK", supplemented by a specific sub-algorithm for hypotension, in the management of hypotension when it occurs in association with anaesthesia.
Reports of hypotension during anaesthesia were extracted and studied from the first 4000 incidents reported to the Australian Incident Monitoring Study (AIMS). The potential performance of the COVER ABCD algorithm and the sub-algorithm for hypotension was compared with the actual management as reported by the anaesthetist involved.
There were 438 reports that mentioned hypotension, cardiovascular collapse, or cardiac arrest. In 17% of reports more than one cause was attributed and 550 causative events were identified overall. The most common causes identified were drugs (26%), regional anaesthesia (14%), and hypovolaemia (9%). Concomitant changes were reported in heart rate or rhythm in 39% and oxygen saturation or ventilation in 21% of reports. Cardiac arrest was documented in 25% of reports. As hypotension was frequently associated with abnormalities of other vital signs, it could not always be adequately addressed by a single algorithm. The sub-algorithm for hypotension is adequate when hypotension occurs in association with sinus tachycardia. However, when it occurs in association with bradycardia, non-sinus tachycardia, desaturation or signs of anaphylaxis or other problems, the sub-algorithm for hypotension recommends cross referencing to other relevant sub-algorithms. It was considered that, correctly applied, the core algorithm COVER ABCD would have diagnosed 18% of cases and led to resolution in two thirds of these. It was further estimated that completion of this followed by the specific sub-algorithm for hypotension would have led to earlier recognition of the problem and/or better management in 6% of cases compared with actual management reported.
Pattern recognition in most cases enables anaesthetists to determine the cause and manage hypotension. However, an algorithm based approach is likely to improve the management of a small proportion of atypical but potentially life threatening cases. While an algorithm based approach will facilitate crisis management, the frequency of co-existing abnormalities in other vital signs means that all cases of hypotension cannot be dealt with using a single algorithm. Diagnosis, in particular, may potentially be assisted by cross referencing to the specific sub-algorithms for these.
低血压在麻醉和手术过程中很常见。未经纠正且持续的低血压会使大脑、心脏、肾脏以及孕期胎儿面临永久性甚至致命损伤的风险。对其进行识别和纠正至关重要，尤其是对于那些已有疾病且影响器官灌注的患者。
探讨一种先前描述的核心算法“COVER ABCD - A SWIFT CHECK”，辅以针对低血压的特定子算法，在与麻醉相关的低血压管理中的作用。
从向澳大利亚事件监测研究（AIMS）报告的前4000起事件中提取并研究麻醉期间低血压的报告。将COVER ABCD算法及低血压子算法的潜在性能与参与麻醉的人员报告的实际管理情况进行比较。
有438份报告提及低血压、心血管虚脱或心脏骤停。在17%的报告中归因于多种原因，总共识别出550个致病事件。确定的最常见原因是药物（26%）、区域麻醉（14%）和低血容量（9%）。在39%的报告中记录了心率或节律的伴随变化，21%记录了血氧饱和度或通气的变化。25%的报告中有心脏骤停的记录。由于低血压常与其他生命体征异常相关，单一算法不一定总能充分处理。当低血压与窦性心动过速相关时，低血压子算法是适用的。然而，当与心动过缓、非窦性心动过速、血氧饱和度下降或过敏反应迹象或其他问题相关时，低血压子算法建议参考其他相关子算法。据认为，正确应用核心算法COVER ABCD可诊断18%的病例，其中三分之二可得到解决。进一步估计，在此基础上完成针对低血压的特定子算法，与报告的实际管理情况相比，可使6%的病例更早识别问题和/或得到更好的管理。
在大多数情况下，模式识别可使麻醉师确定病因并处理低血压。然而，基于算法的方法可能会改善一小部分非典型但可能危及生命的病例的管理。虽然基于算法的方法将有助于危机管理，但其他生命体征中并存异常的频率意味着并非所有低血压病例都能用单一算法处理。特别是诊断可能需要参考针对这些情况的特定子算法。
