Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea.
BUD.on Inc., Jeonju 54871, Korea.
Int J Environ Res Public Health. 2021 Sep 1;18(17):9229. doi: 10.3390/ijerph18179229.
We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV ( = 67) and patients who did not ( = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.
我们评估了来自生物信号的新特征，这些特征包含了多样化的生理反应信息，以预测从机械通气（MV）中脱机的结果。我们招募了 89 名在重症监护病房中接受 MV 脱机的患者，并在自主呼吸试验（SBT）期间收集了连续的生物信号数据：心电图（ECG）、呼吸阻抗、光容积描记图（PPG）、动脉血压和呼吸机参数。我们比较了成功停止 MV 的患者（n=67）和未成功停止 MV 的患者（n=22）之间收集的生物信号数据的变异性。为了评估所识别因素对预测脱机成功的有用性，我们开发了一个机器学习模型，并通过自举法评估了其性能。在 Poincaré 图中，心率变异性的短期和长期标准差之间的比率、ECG 和 PPG 的样本熵、ECG 的α值以及去趋势波动分析中的呼吸阻抗，在脱机成功和失败组之间存在差异。该模型的受试者工作特征曲线下面积为 0.81（95%置信区间：0.70-0.92）。这种基于 SBT 期间的生物信号数据的标记物的组合，为协助临床医生确定最佳拔管时间提供了一种很有前途的工具。
