Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016, Zhejiang, China.
Department of Surgery, 2D, Walter C Mackenzie Health Sciences Centre, University of Alberta, Edmonton, AB, Canada.
Intensive Care Med. 2019 Jun;45(6):856-864. doi: 10.1007/s00134-019-05627-9. Epub 2019 May 6.
Protective mechanical ventilation based on multiple ventilator parameters such as tidal volume, plateau pressure, and driving pressure has been widely used in acute respiratory distress syndrome (ARDS). More recently, mechanical power (MP) was found to be associated with mortality. The study aimed to investigate whether MP normalized to predicted body weight (norMP) was superior to other ventilator variables and to prove that the discrimination power cannot be further improved with a sophisticated machine learning method.
The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The data was split 3:1 into training and testing subsamples. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. The gradient boosting machine was used to examine whether the discrimination could be further improved.
A total of 5159 patients with acute onset ARDS were included for analysis. The discrimination of norMP in predicting mortality was significantly better than the absolute MP (p = 0.011 for DeLong's test). The gradient boosting machine was not able to improve the discrimination as compared to norMP (p = 0.913 for DeLong's test). The multivariable regression model showed a significant interaction between norMP and ARDS severity (p < 0.05). While the norMP was not significantly associated with mortality outcome (OR 0.99; 95% CI 0.91-1.07; p = 0.862) in patients with mild ARDS, it was associated with increased risk of mortality in moderate (OR 1.11; 95% CI 1.02-1.23; p = 0.021) and severe (OR 1.13; 95% CI 1.03-1.24; p < 0.008) ARDS.
The study showed that norMP was a good ventilator variable associated with mortality, and its predictive discrimination cannot be further improved with a sophisticated machine learning method. Further experimental trials are needed to investigate whether adjusting ventilator variables according to norMP will significantly improve clinical outcomes.
基于潮气量、平台压和驱动压等多种呼吸机参数的保护性机械通气已广泛应用于急性呼吸窘迫综合征（ARDS）。最近，机械功率（MP）与死亡率相关。本研究旨在探讨机械功率归一化到预测体重（norMP）是否优于其他呼吸机变量，并证明使用复杂的机器学习方法无法进一步提高鉴别能力。
该研究纳入了由 ARDSNet 进行的八项随机对照试验的个体患者数据。将数据按照 3:1 的比例分为训练和测试子样本。在测试子样本中使用接受者操作特征曲线下面积计算每个呼吸机变量的鉴别能力。梯度提升机用于检查鉴别能力是否可以进一步提高。
共纳入 5159 例急性起病 ARDS 患者进行分析。norMP 预测死亡率的鉴别能力明显优于绝对 MP（DeLong 检验，p=0.011）。与 norMP 相比，梯度提升机无法提高鉴别能力（DeLong 检验，p=0.913）。多变量回归模型显示 norMP 与 ARDS 严重程度之间存在显著交互作用（p<0.05）。在轻度 ARDS 患者中，norMP 与死亡率结局无显著相关性（OR 0.99；95%CI 0.91-1.07；p=0.862），但在中度（OR 1.11；95%CI 1.02-1.23；p=0.021）和重度（OR 1.13；95%CI 1.03-1.24；p<0.008）ARDS 患者中，norMP 与死亡率增加相关。
本研究表明 norMP 是与死亡率相关的良好呼吸机变量，其预测鉴别能力无法通过复杂的机器学习方法进一步提高。需要进一步的实验研究来探讨根据 norMP 调整呼吸机变量是否会显著改善临床结局。
