Predictors of extubation failure in neurocritical patients: A systematic review

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

Effects of proprioceptive neuromuscular facilitation combined with threshold inspiratory muscle training on respiratory function in neurocritical patients with weaning failure: a randomized controlled trial

The purpose of this study was to determine the effects of combining proprioceptive neuromuscular facilitation (PNF) with threshold inspiratory muscle training (TIMT), compared with TIMT alone, on respiratory function in neurocritical patients who experienced a weaning failure. Forty-seven participants (mostly after a stroke), were randomly divided into the experimental group ( n  = 24) and the control group ( n  = 23). The control group received usual care and TIMT, whereas the experimental group, in addition, underwent four 90-s periods of manual PNF. Both groups performed training in the ICU twice a day for 5 consecutive days. The main outcome measures included maximum inspiratory pressure, diaphragmatic excursions, diaphragm thickening fraction, oxygenation index, and forced expiratory volume in 1 s/forced vital capacity. The results showed a significant group-by-time interaction effect for maximum inspiratory pressure [ F (1, 45) = 17.84, η2  = 0.328, P  < 0.001] and oxygenation index [ F [1, 45) = 5.58, η2  = 0.11, P  = 0.023]. When compared with the control group, the experimental group showed overall significantly higher maximum inspiratory pressure [mean difference = 4.37 cm H 2 O, 95% confidence interval (CI) 0.25-8.50, P  = 0.038]. No other significant group differences were found. Combining PNF with TIMT may improve respiratory function in neurocritical patients with weaning failure. This combination approach may increase the likelihood of survival of neurocritical patients in the ICU.

Liberation from Mechanical Ventilation and Tracheostomy Practice in Traumatic Brain Injury

Liberating patients with severe traumatic brain injury (TBI) from mechanical ventilation is often a challenging task. These patients frequently require prolonged ventilation and have persistent alterations in the level and content of consciousness. Questions about their ability to protect their airway are common. Pulmonary complications and copious respiratory secretions are also very prevalent. Thus, it is hardly surprising that rates of extubation failure are high. This is a major problem because extubation failure is associated with a host of poor outcome measures. When the safety of an extubation attempt is uncertain, direct tracheostomy is favored by some, but there is no evidence that this practice leads to better outcomes. Current knowledge is insufficient to reliably predict extubation outcomes in TBI, and practices vary substantially across trauma centers. Yet observational studies provide relevant information that must be weighted when considering the decision to attempt extubation in patients with head injury. This review discusses available evidence on liberation from mechanical ventilation in TBI, proposes priorities for future research, and offers practical advice to guide decisions at the bedside.