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Objectives: Mechanical circulatory support (MCS) is often required to stabilize therapy-refractory cardiogenic shock patients. Left ventricular (LV) unloading by mechanical ventricular support (MVS) via percutaneous devices, such as with Impella® axial pumps, alone or in combination with extracorporeal life support (ECLS, ECMELLA approach), has emerged as a potential clinical breakthrough in the field. While the weaning from MCS is essentially based on the evaluation of circulatory stability of patients, weaning from MVS holds a higher complexity, being dependent on bi-ventricular function and its adaption to load. As a result of this, weaning from MVS is mostly performed in the absence of established algorithms. MVS via Impella is applied in several cardiogenic shock etiologies, such as acute myocardial infarction (support over days) or acute fulminant myocarditis (prolonged support over weeks, PROPELLA). The time point of weaning from Impella in these cohorts of patients remains unclear. We here propose a novel cardiovascular physiology-based weaning algorithm for MVS. Methods: The proposed algorithm is based on the experience gathered at our center undergoing an Impella weaning between 2017 and 2020. Before undertaking a weaning process, patients must had been ECMO-free, afebrile, and euvolemic, with hemodynamic stability guaranteed in the absence of any inotropic support. The algorithm consists of 4 steps according to the acronym TIDE: (i) Transthoracic echocardiography under full Impella-unloading; (ii) Impella rate reduction in single 8–24 h-steps according to patients hemodynamics (blood pressure, heart rate, and ScVO 2 ), including a daily echocardiographic assessment at minimal flow (P2); (iii) Dobutamine stress-echocardiography; (iv) Right heart catheterization at rest and during Exercise-testing via handgrip. We here present clinical and hemodynamic data (including LV conductance data) from paradigmatic weaning protocols of awake patients admitted to our intensive care unit with cardiogenic shock. We discuss the clinical consequences of the TIDE algorithm, leading to either a bridge-to-recovery, or to a bridge-to-permanent LV assist device (LVAD) and/or transplantation. With this protocol we were able to wean 74.2% of the investigated patients successfully. 25.8% showed a permanent weaning failure and became LVAD candidates. Conclusions: The proposed novel cardiovascular physiology-based weaning algorithm is based on the characterization of the extent and sustainment of LV unloading reached during hospitalization in patients with cardiogenic shock undergoing MVS with Impella in our center. Prospective studies are needed to validate the algorithm.

In this randomized trial, proportional-assist ventilation with load-adjustable gain factors did not differ significantly from pressure-support ventilation with respect to the time to liberation from 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.

After the underlying cause of respiratory failure in critically ill patients has been addressed, the priority is to minimize the duration of mechanical ventilation. This review outlines strategies and interventions to reduce this duration. In the United States, almost 800,000 patients who are hospitalized each year require mechanical ventilation. 1 This estimate excludes neonates, and there is little doubt that mechanical ventilation will be increasingly used as the number of patients 65 years of age or older continues to increase. 2 , 3 The majority of patients who receive mechanical ventilation have acute respiratory failure in the postoperative period, pneumonia, congestive heart failure, sepsis, trauma, or the acute respiratory distress syndrome (ARDS). 4 Our discussion below assumes that physicians have addressed metabolic, inflammatory, and infectious conditions that may be present and have corrected them to the extent possible. . . .

BackgroundCurrent guidelines for the treatment of adult severe traumatic brain injury (sTBI) consist of high-quality evidence reports, but they are no longer accompanied by management protocols, as these require expert opinion to bridge the gap between published evidence and patient care. We aimed to establish a modern sTBI protocol for adult patients with both intracranial pressure (ICP) and brain oxygen monitors in place.MethodsOur consensus working group consisted of 42 experienced and actively practicing sTBI opinion leaders from six continents. Having previously established a protocol for the treatment of patients with ICP monitoring alone, we addressed patients who have a brain oxygen monitor in addition to an ICP monitor. The management protocols were developed through a Delphi-method-based consensus approach and were finalized at an in-person meeting.ResultsWe established three distinct treatment protocols, each with three tiers whereby higher tiers involve therapies with higher risk. One protocol addresses the management of ICP elevation when brain oxygenation is normal. A second addresses management of brain hypoxia with normal ICP. The third protocol addresses the situation when both intracranial hypertension and brain hypoxia are present. The panel considered issues pertaining to blood transfusion and ventilator management when designing the different algorithms.ConclusionsThese protocols are intended to assist clinicians in the management of patients with both ICP and brain oxygen monitors but they do not reflect either a standard-of-care or a substitute for thoughtful individualized management. These protocols should be used in conjunction with recommendations for basic care, management of critical neuroworsening and weaning treatment recently published in conjunction with the Seattle International Brain Injury Consensus Conference.

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management.

Background: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. Objectives: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. Methods: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital’s Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., “patients weaned within 24 h” (n = 1042) and “patients not weaned within 24 h” (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. Results: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. Conclusions: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.

Aim This study intends to screen serological indicators related to weaning outcomes and 30-day mortality in severe intracerebral hemorrhage (ICH) patients, use machine learning (ML) algorithms to construct predictive models, and explore their predictive value. Methods Data for this study were derived from the Medical Information Mart for Intensive Care (MIMIC)-IV database, which was divided into training and testing sets at a 7:3 ratio. Random Forest (RF) feature importance ranking was used to evaluate feature importance. Nine ML algorithms were applied to construct predictive models for successful weaning/30-day mortality. Results A total of 1,058 participants were enrolled in this study, among whom 242 achieved successful weaning. In the testing set, among the 9 ML models for predicting successful weaning, extreme Gradient Boosting (XGBoost) showed the best predictive performance, with an AUC of 0.580, an accuracy of 0.582, a sensitivity of 0.522, and a specificity of 0.598. Among the 9 ML models for predicting 30-day mortality, RF showed the best predictive performance, with an AUC of 0.693, an accuracy of 0.642, a sensitivity of 0.627, and a specificity of 0.656. In the DCA curve for predicting successful weaning, within the threshold range of 20%–30%, the clinical net benefits of Decision Tree (DT), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), XGBoost, and Ridge were all higher than those of the “treat all” and “treat none” models. Conclusion XGBoost and RF exhibited relatively better performance among tested models in predicting successful weaning and 30-day mortality, respectively. Weight, glucose, WBC, MCHC, and temperature were the five key influencing factors for successful weaning. Age, APS III, glucose, WBC, and LODS were the most critical prognostic factors for 30-day mortality. This study provides a concise and practical reference tool for clinical risk stratification and decision-making.

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support.

X-linked myotubular myopathy (XLMTM) is a rare, life-threatening congenital myopathy. Most (80%) children with XLMTM have profound muscle weakness and hypotonia at birth resulting in severe respiratory insufficiency, the inability to sit up, stand or walk, and early mortality. At birth, 85–90% of children with XLMTM require mechanical ventilation, with more than half requiring invasive ventilator support. Historically, ventilator-dependent children with neuromuscular-derived respiratory failure of this degree and nature, static or progressive, are not expected to achieve complete independence from mechanical ventilator support. In the ASPIRO clinical trial (NCT03199469), participants receiving a single intravenous dose of an investigational gene therapy (resamirigene bilparvovec) started showing significant improvements in daily hours of ventilation support compared with controls by 24 weeks post-dosing, and 16 of 24 dosed participants achieved ventilator independence between 14 and 97 weeks after dosing. At the time, there was no precedent or published guidance for weaning chronically ventilated children with congenital neuromuscular diseases off mechanical ventilation. When the first ASPIRO participants started showing dramatically improved respiratory function, the investigators initiated efforts to safely wean them off ventilator support, in parallel with primary protocol respiratory outcome measures. A group of experts in respiratory care and physiology and management of children with XLMTM developed an algorithm to safely wean children in the ASPIRO trial off mechanical ventilation as their respiratory muscle strength increased. The algorithm developed for this trial provides recommendations for assessing weaning readiness, a stepwise approach to weaning, and monitoring of children during and after the weaning process.