Articles: mechanical-ventilation.
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Pneumomediastinum, subcutaneous emphysema and pneumothorax are not rarely observed during the COVID-19 pandemic. Such complications can worsen gas exchange and the overall prognosis in critical patients. The aim of this study is to investigate what predisposing factors are related to pneumomediastinum and pneumothorax in SARS-CoV2-Acute Respiratory Distress Syndrome (ARDS), what symptoms may predict a severe and potentially fatal complication and what therapeutical approach may provide a better outcome. ⋯ HFNC is a safe and effective ventilatory approach for critical COVID-19 and has a positive role in associated complications such as pneumomediastinum and pneumothorax.
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The surge of critically ill patients due to the coronavirus disease-2019 (COVID-19) overwhelmed critical care capacity in areas of northern Italy. Anesthesia machines have been used as alternatives to traditional ICU mechanical ventilators. However, the outcomes for patients with COVID-19 respiratory failure cared for with Anesthesia Machines is currently unknow. We hypothesized that COVID-19 patients receiving care with Anesthesia Machines would have worse outcomes compared to standard practice. ⋯ Not applicable.
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Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. ⋯ The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65-0.69), and 0.69 (0.67-0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.