Predictors of extubation failure in neurocritical patients: A systematic review

Ultrasound-based abdominal muscles and diaphragm assessment in predicting extubation failure in patients requiring neurointensive care: a single-center observational study

Extubation failure rates are notably high in patients in neurointensive care. Ineffective cough is the variable independently associated with extubation failure, but its quantification remains challenging. Patients with primary central nervous system injury requiring invasive mechanical ventilation were included. After a successful spontaneous breathing trial (SBT), abdominal muscles and diaphragm ultrasound were performed under tidal breathing and coughing. 98 patients were initially recruited for the study, and 40 patients were ultimately included in the final analysis. Extubation failure occurred in 8 (20%) patients. Rectus abdominis (RA) and internal oblique (IO) muscles showed difference regarding cough thickening fraction (TF) between the extubation success and failure group (P < 0.05). The logistic regression that analysis suggested cough TFRA, cough TFIO and cough TIO were the factors associated with extubation outcome (P < 0.05). In the receiver operating characteristic analysis, cough TFIO exhibited the strongest predictive value (AUC = 0.957, 95% CI:0.8979-1). A threshold of cough TFIO ≥ 34.15% predicted extubation success with a sensitivity of 93.8% and specificity of 75%. Abdominal muscles ultrasound was a promising tool to predict extubation for patients requiring neurointensive care.Trial registration: The study was registered on Chinese Clinical Trial Registry: ChiCTR2400088210, Registered 13 August 2024 - Retrospectively registered, https://www.chictr.org.cn/bin/project/edit?pid=234150 .

Predictors of weaning failure in ventilated intensive care patients: a systematic evidence map

BACKGROUND

Ventilator weaning is of great importance for intensive care patients in order to avoid complications caused by prolonged ventilation. However, not all patients succeed in weaning immediately. Their spontaneous breathing may be insufficient, resulting in extubation failure and the subsequent need for reintubation. To identify patients at high risk for weaning failure, a variety of potential predictors has already been examined in individual studies and meta-analyses over the last decades. However, an overview of all the predictors investigated is missing.

AIM

To provide an overview of empirically investigated predictors for weaning failure.

METHODS

A systematic evidence map was developed. To this end, we conducted a systematic search in the Medline, Cochrane, and CINAHL databases in December 2023 and added a citation search and a manual search in June 2024. Studies on predictors for weaning failure in adults ventilated in the intensive care unit were included. Studies on children, outpatients, non-invasive ventilation, or explanatory factors of weaning failure were excluded. Two reviewers performed the screening and data extraction independently. Data synthesis followed an inductive approach in which the predictors were thematically analyzed, sorted, and clustered.

RESULTS

Of the 1388 records obtained, 140 studies were included in the analysis. The 112 prospective and 28 retrospective studies investigated a total of 145 predictors. These were assigned to the four central clusters 'Imaging procedures' (n = 22), 'Physiological parameters' (n = 61), 'Scores and indices' (n = 53), and 'Machine learning models' (n = 9). The most frequently investigated predictors are the rapid shallow breathing index, the diaphragm thickening fraction, the respiratory rate, the P/F ratio, and the diaphragm excursion.

CONCLUSION

Predictors for weaning failure are widely researched. To date, 145 predictors have been investigated with varying intensity in 140 studies that are in line with the current weaning definition. It is no longer just individual predictors that are investigated, but more comprehensive assessments, indices and machine learning models in the last decade. Future research should be conducted in line with international weaning definitions and further investigate poorly researched predictors. Registration, Protocol: https://doi.org/10.17605/OSF.IO/2KDYU.

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.