International Variation in Intubation and Extubation Practices and Adverse Events Among Critically Ill Patients Receiving Mechanical Ventilation

Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.

To Wean or Not to Wean: A Practical Patient Focused Guide to Ventilator Weaning

Since the inception of critical care medicine and artificial ventilation, literature and research on weaning has transformed daily patient care in intensive care units (ICU). As our knowledge of mechanical ventilation (MV) improved, so did the need to study patient-ventilator interactions and weaning predictors. Randomized trials have evaluated the use of protocol-based weaning (vs. usual care) to study the duration of MV in ICUs, different techniques to conduct spontaneous breathing trials (SBT), and strategies to eventually extubate a patient whose initial SBT failed. Despite considerable milestones in the management of multiple diseases contributing to reversible respiratory failure, in the application of early rehabilitative interventions to preserve muscle integrity, and in ventilator technology that mitigates against ventilator injury and dyssynchrony, major barriers to successful liberation from MV persist. This review provides a broad encompassing view of weaning classification, causes of weaning failure, and evidence behind weaning predictors and weaning modes.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

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 (n = 67) and patients who did not (n = 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.