使用波形分析评估重症监护医师识别患者-呼吸机不同步的能力：一项全国性调查。
The Ability of Critical Care Physicians to Identify Patient-Ventilator Asynchrony Using Waveform Analysis: A National Survey.
机构信息
University of Sousse, Faculty of Medicine of Sousse, 4002, Sousse, Tunisia; and Farhat Hached University Hospital, Medical Intensive Care Unit, Research Laboratory "Heart Failure," LR12SP09, 4000, Sousse, Tunisia.
University of Monastir, Faculty of Medicine of Monastir, Monastir, Tunisia; and Pediatric Department, Fattouma Bourguiba University Hospital, Monastir, Tunisia; University of Medicine of Monastir, Monastir, Tunisia.
出版信息
Respir Care. 2024 Jan 24;69(2):176-183. doi: 10.4187/respcare.11360.
BACKGROUND
Improved patient-ventilator asynchrony (PVA) identification using waveform analysis by critical care physicians (CCPs) may improve patient outcomes. This study aimed to assess the ability of CCPs to identify different types of PVAs using waveform analysis as well as factors related to this ability.
METHODS
We surveyed 12 university-affiliated medical ICUs (MICUs) in Tunisia. CCPs practicing in these MICUs were asked to visually identify 4 clinical cases, each corresponding to a different PVA. We collected the following characteristics regarding CCPs: scientific grade, years of experience, prior training in mechanical ventilation, prior exposure to waveform analysis, and the characteristics of the MICUs in which they practice. Respondents were categorized into 2 groups based on their ability to correctly identify PVAs (defined as the correct identification of at least 3 of the 4 PVA cases). Univariate analysis was performed to identify factors related to the correct identification of PVA.
RESULTS
Among 136 included CCPs, 72 (52.9%) responded to the present survey. The respondents comprised 59 (81.9%) residents, and 13 (18.1%) senior physicians. Further, 50 (69.4%) respondents had attended prior training in mechanical ventilation. Moreover, 21 (29.2%) of the respondents could correctly identify PVAs. Double-triggering was the most frequently identified PVA type, 43 (59.7%), followed by auto-triggering, 36 (50%); premature cycling, 28 (38.9%); and ineffective efforts, 25 (34.7%). Univariate analysis indicated that senior physicians had a better ability to correctly identify PVAs than residents (7 [53.8%] vs 14 [23.7%], = .044).
CONCLUSIONS
The present study revealed a significant deficiency in the accurate visual identification of PVAs among CCPs in the MICUs. When compared to residents, senior physicians exhibited a notably superior aptitude for correctly recognizing PVAs.
背景
使用波形分析由重症监护医生（CCP）识别改善的患者-呼吸机不同步（PVA）可能改善患者预后。本研究旨在评估 CCP 使用波形分析识别不同类型 PVA 的能力以及与这种能力相关的因素。
方法
我们调查了突尼斯的 12 家大学附属医疗 ICU（MICUs）。要求在这些 MICUs 工作的 CCP 对 4 个临床病例进行视觉识别，每个病例对应不同的 PVA。我们收集了 CCP 的以下特征：科学职称、工作年限、机械通气培训经历、波形分析经验以及他们所在的 MICUs 的特征。根据他们正确识别 PVA 的能力，将受访者分为 2 组（定义为正确识别至少 4 个 PVA 病例中的 3 个）。进行单变量分析以确定与正确识别 PVA 相关的因素。
结果
在 136 名被纳入的 CCP 中，有 72 名（52.9%）对本次调查做出了回应。受访者包括 59 名（81.9%）住院医师和 13 名（18.1%）高级医师。此外，50 名（69.4%）受访者参加过机械通气培训。此外，有 21 名（29.2%）受访者能够正确识别 PVA。双重触发是最常被识别的 PVA 类型，有 43 名（59.7%），其次是自动触发，有 36 名（50%）；过早循环，有 28 名（38.9%）；无效努力，有 25 名（34.7%）。单变量分析表明，高级医师正确识别 PVA 的能力明显优于住院医师（7[53.8%]比 14[23.7%]， =.044）。
结论
本研究显示，MICUs 中的 CCP 准确视觉识别 PVA 的能力存在明显不足。与住院医师相比，高级医师在正确识别 PVA 方面表现出明显更高的能力。
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