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缺乏连续视频脑电图监测导致事件报告延迟。

Lack of Continuous Video EEG Surveillance Results in Delayed Event Reporting.

机构信息

Epilepsy Division, Department of Neurology, University of Mississippi, Jackson, Mississippi.

Epilepsy Division, Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, New York.

出版信息

Neurodiagn J. 2024 Sep;64(3):122-129. doi: 10.1080/21646821.2024.2375477. Epub 2024 Jul 16.

DOI:10.1080/21646821.2024.2375477
PMID:39012963
Abstract

Although real-time event detection during video EEG recording is required to ensure patients' safety, it is limited by the technologists' availability. We sought to explore the efficiency of real-time event detection by the EEG technologists in a single tertiary academic center. We retrospectively reviewed events from continuous inpatient video EEGs (cEEGs) and epilepsy monitoring unit (EMU) recordings in January 2017, when real-time surveillance was only available during the night shift, and June 2017, when a dedicated neurodiagnostic EEG technologist was available for real-time monitoring during all shifts. The events were categorized into those detected immediately (eyes-on), later in the same shift (delayed) or identified on the subsequent shift (missed). Chi-square and Fisher's exact tests were used for statistical comparisons. In January 2017, there were 25 patients (117 days of monitoring) in the EMU and 54 inpatients (146 days of monitoring) on cEEG with 92 total events, (39% seizures). In June 2017, there were 30 patients (133 days of monitoring) in the EMU and 47 additional inpatients (80 days of monitoring) on cEEG with 110 total events, (39% seizures). The number of events identified in real time was low and did not significantly differ among shifts regardless of the availability of the monitoring technologist. Most events were identified at the time of subsequent EEG scanning by the EEG technologist. Partial staffing for continuous video EEG surveillance is insufficient to identify events in real time. EEG technologists are able to identify events during regular EEG scanning.

摘要

尽管在视频脑电图 (video EEG) 记录期间进行实时事件检测对于确保患者安全是必要的,但这受到技术员可用性的限制。我们试图探索在单一三级学术中心中,脑电图技术员实时检测事件的效率。我们回顾性地分析了 2017 年 1 月(仅在夜班期间提供实时监测)和 2017 年 6 月(全天各时段均有专门的神经诊断脑电图技术员进行实时监测)连续住院视频脑电图 (cEEG) 和癫痫监测单元 (EMU) 记录中的事件。将事件分为立即检测到的(实时)、同一班次中延迟检测到的(延时)或下一班次中发现的(漏检)。使用卡方检验和 Fisher 精确检验进行统计学比较。在 2017 年 1 月,EMU 中有 25 名患者(监测 117 天)和 54 名 cEEG 住院患者(监测 146 天),共发生 92 起事件(39%为癫痫发作)。在 2017 年 6 月,EMU 中有 30 名患者(监测 133 天)和 47 名额外的 cEEG 住院患者(监测 80 天),共发生 110 起事件(39%为癫痫发作)。实时识别的事件数量很少,且无论监测技术员是否在场,各班次之间均无显著差异。大多数事件是在脑电图技术员随后进行 EEG 扫描时识别出来的。对连续视频脑电图监测进行部分人员配置不足以实时识别事件。脑电图技术员能够在常规 EEG 扫描期间识别事件。

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