Critical care medicine
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Critical care medicine · Nov 2020
Randomized Controlled Trial Multicenter StudyA Randomized Controlled Trial of Antithrombin Supplementation During Extracorporeal Membrane Oxygenation.
Supplementation of antithrombin might decrease the amount of heparin needed to achieve a given anticoagulation target during extracorporeal membrane oxygenation. However, exogenous antithrombin itself may increase the risk of bleeding. We conceived a study to evaluate the effect of antithrombin supplementation in adult patients requiring venovenous extracorporeal membrane oxygenation for respiratory failure on heparin dose, adequacy of anticoagulation, and safety. ⋯ Antithrombin supplementation may not decrease heparin requirement nor diminish the incidence of bleeding and/or thrombosis in adult patients on venovenous extracorporeal membrane oxygenation.
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Critical care medicine · Nov 2020
Multicenter StudyHigh-Flow Nasal Oxygen in Coronavirus Disease 2019 Patients With Acute Hypoxemic Respiratory Failure: A Multicenter, Retrospective Cohort Study.
An ongoing outbreak of coronavirus disease 2019 is spreading globally. Acute hypoxemic respiratory failure is the most common complication of coronavirus disease 2019. However, the clinical effectiveness of early high-flow nasal oxygen treatment in patients with coronavirus disease 2019 with acute hypoxemic respiratory failure has not been explored. This study aimed to analyze the effectiveness of high-flow nasal oxygen treatment and to identify the variables predicting high-flow nasal oxygen treatment failure in coronavirus disease 2019 patients with acute hypoxemic respiratory failure. ⋯ High-flow nasal oxygen may be effective for treating coronavirus disease 2019 patients with mild to moderate acute hypoxemic respiratory failure. However, high-flow nasal oxygen failure was associated with a poor prognosis. Male and lower oxygenation at admission were the two strong predictors of high-flow nasal oxygen failure.
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Critical care medicine · Nov 2020
Multicenter Study Observational StudyAn Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.
Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. ⋯ Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.
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Critical care medicine · Nov 2020
Multicenter StudyThe Association Between Endotracheal Tube Size and Aspiration (During Flexible Endoscopic Evaluation of Swallowing) in Acute Respiratory Failure Survivors.
To determine whether a modifiable risk factor, endotracheal tube size, is associated with the diagnosis of postextubation aspiration in survivors of acute respiratory failure. ⋯ Larger endotracheal tube size was associated with increased risk of aspiration and laryngeal granulation tissue. Using smaller endotracheal tubes may reduce the risk of postextubation aspiration.
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Critical care medicine · Nov 2020
Multicenter StudyThe Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.
Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record. ⋯ Our novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.