Association Between Ventilatory Settings and Development of Acute Respiratory Distress Syndrome in Mechanically Ventilated Patients Due to Brain Injury

PURPOSE: In neurologically critically ill patients with mechanical ventilation (MV), the development of acute respiratory distress syndrome (ARDS) is a major contributor to morbidity and mortality, but the role of ventilatory management has been scarcely evaluated. We evaluate the association of tidal volume, level of PEEP and driving pressure with the development of ARDS in a population of patients with brain injury. MATERIALS AND METHODS: We performed a secondary analysis of a prospective, observational study on mechanical ventilation. RESULTS: We included 986 patients mechanically ventilated due to an acute brain injury (hemorrhagic stroke, ischemic stroke or brain trauma). Incidence of ARDS in this cohort was 3%. Multivariate analysis suggested that driving pressure could be associated with the development of ARDS (odds ratio for unit increment of driving pressure 1.12; confidence interval for 95%: 1.01 to 1.23) whereas we did not observe association for tidal volume (in ml per kg of predicted body weight) or level of PEEP. ARDS was associated with an increase in mortality, longer duration of mechanical ventilation, and longer ICU length of stay. CONCLUSIONS: In a cohort of brain-injured patients the development of ARDS was not common. Driving pressure was associated with the development of this disease.info:eu-repo/semantics/publishedVersio

Heat and moisture exchangers (HMEs) and heated humidifiers (HHs) in adult critically ill patients: a systematic review, meta-analysis and meta-regression of randomized controlled trials

The aims of this systematic review and meta-analysis of randomized controlled trials are to evaluate the effects of active heated humidifiers (HHs) and moisture exchangers (HMEs) in preventing artificial airway occlusion and pneumonia, and on mortality in adult critically ill patients. In addition, we planned to perform a meta-regression analysis to evaluate the relationship between the incidence of artificial airway occlusion, pneumonia and mortality and clinical features of adult critically ill patients

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes