Rehm Gregory B, Cortés-Puch Irene, Kuhn Brooks T, Nguyen Jimmy, Fazio Sarina A, Johnson Michael A, Anderson Nicholas R, Chuah Chen-Nee, Adams Jason Y
Department of Computer Science, University of California Davis, Davis, CA.
Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of California Davis, Sacramento, CA.
Crit Care Explor. 2021 Jan 8;3(1):e0313. doi: 10.1097/CCE.0000000000000313. eCollection 2021 Jan.
To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data.
Retrospective, observational cohort study.
Academic medical center ICU.
Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure.
None.
Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6).
Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.
开发并描述一种机器学习算法，该算法仅使用呼吸机波形数据就能将急性呼吸窘迫综合征与其他呼吸衰竭原因区分开来。
回顾性观察队列研究。
学术医疗中心重症监护病房。
入住重症监护病房需要有创机械通气的成年人，包括50例急性呼吸窘迫综合征患者和50例除低氧性呼吸衰竭外有机械通气主要指征的患者。
无。
在符合急性呼吸窘迫综合征标准后的头24小时（或非急性呼吸窘迫综合征患者机械通气的头24小时）内，对机械通气的压力和流量时间序列数据进行处理，以提取9个生理特征。训练随机森林机器学习算法以区分有无急性呼吸窘迫综合征的患者。使用受试者操作特征曲线下面积、敏感性、特异性、阳性预测值和阴性预测值评估模型性能。分析检查了使用头24小时数据训练模型并使用头24小时（24/24模型）或6小时（24/6模型）的保留数据进行测试时的性能。受试者操作特征曲线下面积、敏感性、特异性、阳性预测值和阴性预测值分别为0.88、0.90、0.71、0.77和0.90（24/24）；以及0.89、0.90、0.75、0.83和0.83（24/6）。
使用机器学习和从原始呼吸机波形数据中得出的生理信息可能有助于在插管后的早期时间点筛查急性呼吸窘迫综合征。这种方法与传统诊断标准相结合，可以改善急性呼吸窘迫综合征的及时识别，并实现自动化临床决策支持，特别是在传统诊断测试和电子健康记录可用性有限的环境中。
