Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation. (February 2023)

• Record Type: Journal Article
• Authors: Pan, Qing; Zhang, Haoyuan; Jiang, Mengting; Ning, Gangmin; Fang, Luping; Ge, Huiqing
• Is Part Of: Computers in biology and medicine. Volume 153(2023)
• Publication Date: 2023-02
• Subjects: Extubation failure -- Mechanical ventilation -- Variability analysis -- Machine learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106459
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and objective: Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10–15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored. Methods: Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost). Results: The coefficient of variation of the dynamic mechanical power per breath (CV-MPd [J/breath]) exhibited the highest area under the receiver operating characteristic curve (AUC) of 0.777 among the individual indices. Furthermore, the XGBoost model obtained the best AUC (0.902) by combining multiple selected variability indices. Conclusions: These results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients. Highlights: Breathing variability has been proposed to predict extubation failure for decades. Comprehensive breathing variability indices enhance extubation failure prediction. Combination of variability indices further improve extubation failure prediction. The XGBoost model had optimal performance.