Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients’ ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6–88.3], and 86.8% [86.6–87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

中文翻译：

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通过监督深度学习技术检测方流辅助通气期间的流量不足

流量不足是一种患者与呼吸机不同步的情况，当由于气流不足和/或高吸气努力而导致气体输送不能完全满足患者的通气需求时，通常会通过目视检查气道压力波形来识别。临床诊断繁琐且容易漏诊，这为人工智能提供了机会。我们的目标是开发一种监督人工智能算法，用于识别方流辅助通气和患者触发呼吸期间的气道压力变形。多中心、观察性研究。包括接受方流辅助通气机械通气 > 24 小时的成年危重患者。作为参考，5位重症监护专家对气道压力变形严重程度进行了分级。使用准确度、精确度、召回率和 F1 分数来训练和评估卷积神经网络和循环神经网络模型。在进行食管压力测量 (ΔPes) 的患者亚组中，我们分析了吸气强度与气道压力变形之间的关联。对 28 名患者的 6428 次呼吸进行了分析，其中 42% 被归类为正常-轻度、23% 中度和 34% 重度气道压力变形。循环神经网络算法和卷积神经网络的准确率分别为87.9%[87.6-88.3]和86.8%[86.6-87.4]。8.8% 的呼吸出现双重触发，且总是存在严重气道压力变形。亚组分析表明，74.4% 被归类为严重气道压力变形的呼吸的 ΔPes > 10 cmH2O，37.2% 的 ΔPes > 15 cmH2O。循环神经网络模型似乎非常适合识别由于流量不足而导致的气道压力变形。它可以用作实时 24 小时床边监测工具，以最大程度地减少无法识别的患者与呼吸机不适当相互作用的时间段。