Department of Electrical and Computer Engineering in Medicine Pediatric Anesthesia Research Team, BC Children's Hospital, 1L7-4480 Oak St., Vancouver, BC V6H 3V4, Canada.
Anesth Analg. 2013 Aug;117(2):380-91. doi: 10.1213/ANE.0b013e3182975b63. Epub 2013 Jun 18.
Perioperative monitoring systems produce a large amount of uninterpreted data, use threshold alarms prone to artifacts, and rely on the clinician to continuously visually track changes in physiological data. To address these deficiencies, we developed an expert system that provides real-time clinical decisions for the identification of critical events. We evaluated the efficacy of the expert system for enhancing critical event detection in a simulated environment. We hypothesized that anesthesiologists would identify critical ventilatory events more rapidly and accurately with the expert system.
We used a high-fidelity human patient simulator to simulate an operating room environment. Participants managed 4 scenarios (anesthetic vapor overdose, tension pneumothorax, anaphylaxis, and endotracheal tube cuff leak) in random order. In 2 of their 4 scenarios, participants were randomly assigned to the expert system, which provided trend-based alerts and potential differential diagnoses. Time to detection and time to treatment were measured. Workload questionnaires and structured debriefings were completed after each scenario, and a usability questionnaire at the conclusion of the session. Data were analyzed using a mixed-effects linear regression model; Fisher exact test was used for workload scores.
Twenty anesthesiology trainees and 15 staff anesthesiologists with a combined median (range) of 36 (29-66) years of age and 6 (1-38) years of anesthesia experience participated. For the endotracheal tube cuff leak, the expert system caused mean reductions of 128 (99% confidence interval [CI], 54-202) seconds in time to detection and 140 (99% CI, 79-200) seconds in time to treatment. In the other 3 scenarios, a best-case decrease of 97 seconds (lower 99% CI) in time to diagnosis for anaphylaxis and a worst-case increase of 63 seconds (upper 99% CI) in time to treatment for anesthetic vapor overdose were found. Participants were highly satisfied with the expert system (median score, 2 on a scale of 1-7). Based on participant debriefings, we identified avoidance of task fixation, reassurance to initiate invasive treatment, and confirmation of a suspected diagnosis as 3 safety-critical areas.
When using the expert system, clinically important and statistically significant decreases in time to detection and time to treatment were observed for the endotracheal tube cuff Leak scenario. The observed differences in the other 3 scenarios were much smaller and not statistically significant. Further evaluation is required to confirm the clinical utility of real-time expert systems for anesthesia.
围手术期监测系统产生大量未经解释的数据，使用容易受到伪影影响的阈值报警，并且依赖于临床医生持续视觉跟踪生理数据的变化。为了解决这些缺陷，我们开发了一个专家系统，为识别关键事件提供实时临床决策。我们评估了该专家系统在模拟环境中增强关键事件检测的效果。我们假设麻醉师使用该专家系统能够更快、更准确地识别关键通气事件。
我们使用高保真人体模拟患者来模拟手术室环境。参与者随机管理 4 个场景（麻醉蒸气过量、张力性气胸、过敏反应和气管插管套囊泄漏）。在他们的 4 个场景中的 2 个中，参与者被随机分配到专家系统，该系统提供基于趋势的警报和潜在的鉴别诊断。测量检测到和治疗的时间。在每个场景后完成工作量问卷和结构化讨论，并在会议结束时完成可用性问卷。使用混合效应线性回归模型分析数据；Fisher 精确检验用于工作量评分。
20 名麻醉学受训者和 15 名麻醉科工作人员参加了此次研究，他们的平均年龄为 36 岁（范围 29-66 岁），平均麻醉经验为 6 年（范围 1-38 年）。对于气管插管套囊泄漏，专家系统使检测时间平均减少 128 秒（99%置信区间 [CI]，54-202 秒），治疗时间减少 140 秒（99%CI，79-200 秒）。在其他 3 个场景中，过敏反应的诊断时间最快减少 97 秒（99%CI 的下限），麻醉蒸气过量的治疗时间最长增加 63 秒（99%CI 的上限）。参与者对专家系统非常满意（评分中位数为 1-7 分制的 2 分）。根据参与者的讨论，我们确定了避免任务固定、放心开始侵入性治疗以及确认疑似诊断作为 3 个安全关键领域。
在使用专家系统时，气管插管套囊泄漏场景中观察到检测时间和治疗时间有显著的临床重要性和统计学意义的减少。在其他 3 个场景中观察到的差异要小得多，且无统计学意义。需要进一步评估来确认实时专家系统在麻醉中的临床实用性。
