Background Traumatic rhabdomyolysis (RM) is common and associated with the development of acute kidney injury and potentially with other organ dysfunctions. Thus, RM may increase the risk of death. The primary objective was to assess the effect of severe RM (Creatine Kinase [CK] > 5000 U/L) on 30-day mortality in trauma patients using a causal inference approach. Methods In this multicenter cohort study conducted in France using a national major trauma registry (Traumabase) between January 1, 2012, and July 1, 2023, all patients admitted to a participating major trauma center hospitalized in intensive care unit (ICU) and with CK measurement were included. Confounding variables for both 30-day mortality and exposure were used to establish a propensity score. A doubly robust approach with inverse treatment weighting enabled the calculation of the average treatment effect on the treated (ATT). Analyses were performed in the overall cohort as well as in two subgroups: hemorrhagic shock sub

Electrical impedance tomography (EIT) is an emerging technology for the non-invasive monitoring of regional distribution of ventilation and perfusion, offering real-time and continuous data that can greatly enhance our understanding and management of various respiratory conditions and lung perfusion. Its application may be especially beneficial for critically ill mechanically ventilated patients. Despite its potential, clear evidence of clinical benefits is still lacking, in part due to a lack of standardization and transparent reporting, which is essential for ensuring reproducible research and enhancing the use of EIT for personalized mechanical ventilation. This report is the result of a four-day expert meeting where we aimed to promote the consistent and reliable use of EIT, facilitating its integration into both clinical practice and research, focusing on the adult intensive care patient. We discuss the state-of-the-art regarding EIT acquisition and processing, applications during

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 chi

Lung injury is closely associated with the heterogeneity, severity, mortality, and prognosis of various respiratory diseases. Effective monitoring of lung injury is crucial for the optimal management and improved outcomes of patients with lung diseases. This review describes acute and chronic respiratory diseases characterized by significant lung injury and current clinical tools for assessing lung health. Furthermore, we summarized the mechanisms of lung cell death observed in these diseases and highlighted recently identified biomarkers in the plasma indicative of injury to specific cell types and scaffold structure in the lung. Last, we propose an artificial intelligence-driven lung injury monitoring model to assess disease severity, and predict mortality and prognosis, aiming to achieve precision and personalized medicine.

Background New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML). Methods The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions. Results Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model