Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; UCL Respiratory, University College London, London, UK.
Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; Chief Executive Officer (CEO), Rural Healthcare Networks, Eastren Province Health Cluster, Saudi Arabia.
Heart Lung. 2020 Sep-Oct;49(5):630-636. doi: 10.1016/j.hrtlng.2020.04.002. Epub 2020 Apr 30.
Patient-ventilator asynchrony (PVA) is a prevalent and often underrecognized problem in mechanically ventilated patients. Ventilator waveform analysis is a noninvasive and reliable means of detecting PVAs, but the use of this tool has not been broadly studied.
Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians.
A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs.
Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.
患者-呼吸机不同步（PVA）是机械通气患者中普遍存在且经常未被识别的问题。呼吸机波形分析是一种检测 PVA 的非侵入性且可靠的方法，但该工具的使用尚未得到广泛研究。
我们的观察性分析利用经过验证的评估工具来评估重症监护医师（CCP）在三个视频中检测不同 PVA 类型的能力。该工具由三个常见 PVA 视频组成（即双重触发、自动触发和无效触发）。数据通过评估表收集，分发给 39 家医院的各种 CCP，包括呼吸治疗师（RT）、护士和医生。
共评估了 411 名 CCP；其中，只有 41 名（10.2%）正确识别了三种 PVA 类型，而 92 名（22.4%）正确识别了两种类型，174 名（42.3%）正确识别了一种类型；25.3%的人没有识别出任何 PVA。在识别三种 PVA 方面，接受过培训和未接受过培训的 CCP 之间存在统计学显著差异（p<0.001）；在识别两种 PVA 方面，接受过培训和未接受过培训的 CCP 之间也存在统计学显著差异（p=0.001）。大多数识别出一种或零种 PVA 的 CCP 未接受过培训，且各组之间存在统计学显著差异（一种 PVA，p=0.001；零种 PVA，p=0.004）。女性性别和先前接受过呼吸机波形培训被发现可增加正确识别两种以上 PVA 的几率，比值比（OR）（95%置信区间[CI]）分别为 1.93（1.07-3.49）和 5.41（3.26-8.98）。职业、经验和医院特征与增加 PVA 检测几率无关；在对 RT 职业应用回归模型后，这种关联仍然成立，先前培训（2.89 [1.28-6.51]）和女性性别（2.49 [1.15-5.39]）的 OR 显示出识别两种或更多 PVA 的几率增加。
在重症监护环境中，常见 PVA 的检测率较低，约 25%的 PVA 未被 CCP 检测到。女性性别和先前接受过呼吸机图形培训是 CCP 和 RT 正确识别 PVA 的唯一显著预测因素。迫切需要建立针对机械通气患者中 PVA 的早期检测和管理的教学和培训计划、政策和指南，以改善患者的预后。
