Critical care : the official journal of the Critical Care Forum
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Randomized Controlled Trial
Early nasal high-flow versus Venturi mask oxygen therapy after lung resection: a randomized trial.
Data on high-flow nasal oxygen after thoracic surgery are limited and confined to the comparison with low-flow oxygen. Different from low-flow oxygen, Venturi masks provide higher gas flow at a predetermined fraction of inspired oxygen (FiO2). We conducted a randomized trial to determine whether preemptive high-flow nasal oxygen reduces the incidence of postoperative hypoxemia after lung resection, as compared to Venturi mask oxygen therapy. ⋯ When compared to Venturi mask after thoracotomic lung resection, preemptive high-flow nasal oxygen did not reduce the incidence of postoperative hypoxemia nor improved other analyzed outcomes. Further adequately powered investigations in this setting are warranted to establish whether high-flow nasal oxygen may yield clinical benefit on carbon dioxide clearance.
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Observational Study
Association of extracerebral organ failure with 1-year survival and healthcare-associated costs after cardiac arrest: an observational database study.
Organ dysfunction is common after cardiac arrest and associated with worse short-term outcome, but its impact on long-term outcome and treatment costs is unknown. ⋯ Extracerebral organ dysfunction is associated with long-term outcome and gross healthcare-associated costs of ICU-treated cardiac arrest patients. It should be considered when assessing interventions to improve outcomes and optimize the use of resources in these patients.
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Multicenter Study Observational Study
The prognostic value of optic nerve sheath diameter in patients with subarachnoid hemorrhage.
We evaluated the role of optic nerve sheath diameter (ONSD) using brain computed tomography (CT) in predicting neurological outcomes of patients with subarachnoid hemorrhage (SAH). ⋯ ONSD measured with CT may be used in combination with clinical grading scales to improve prognostic accuracy in SAH patients.
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Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI). ⋯ Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.