Introduction: Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.
Methods: We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017.
The recent H1N1 epidemic has resulted in a large number of deaths, primarily from acute hypoxemic respiratory failure. We reviewed the current strategies to rescue patients with severe hypoxemia. Included in these strategies are high-frequency oscillatory ventilation, airway pressure release ventilation, inhaled vasodilators, and the use of extracorporeal life support.
View Article and Find Full Text PDFBACKGROUND: The effects of the murine monoclonal anti-PcrV antibody Mab166 on acute lung injury induced by Pseudomonas aeruginosa were analyzed in a rat model. METHODS: Lung injury was induced by the instillation of P. aeruginosa strain PA103 directly into the left lungs of anesthetized rats.
View Article and Find Full Text PDFAcute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are common causes of morbidity and mortality in the intensive care unit. ALI/ARDS occurs as a result of systemic inflammation, usually triggered by a microorganism. Activation of leukocytes and release of proinflammatory mediators from multiple cellular sources result in both local and distant tissue injury.
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