Critical care medicine
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Critical care medicine · Feb 2023
Development of Moral Injury in ICU Professionals During the COVID-19 Pandemic: A Prospective Serial Interview Study.
During the COVID-19 pandemic, ICU professionals have faced moral problems that may cause moral injury. This study explored whether, how, and when moral injury among ICU professionals developed in the course of the COVID-19 pandemic. ⋯ ICU professionals exhibit symptoms of moral injury such as feelings of betrayal, detachment, self-alienation, and disorientation. Healthcare organizations and ICU professionals themselves should be cognizant that these feelings may indicate that professionals might have developed moral injury or that it may yet develop in the future. Awareness should be raised about moral injury and should be followed up by asking morally injured professionals what they need, so as to not risk offering unwanted help.
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Critical care medicine · Feb 2023
Precision Effects of Glibenclamide on MRI Endophenotypes in Clinically Relevant Murine Traumatic Brain Injury.
Addressing traumatic brain injury (TBI) heterogeneity is increasingly recognized as essential for therapy translation given the long history of failed clinical trials. We evaluated differential effects of a promising treatment (glibenclamide) based on dose, TBI type (patient selection), and imaging endophenotype (outcome selection). Our goal to inform TBI precision medicine is contextually timely given ongoing phase 2/planned phase 3 trials of glibenclamide in brain contusion. ⋯ High-dose glibenclamide benefitted hematoma volume, vasogenic edema, cytotoxic edema, and BBB integrity after isolated brain contusion. Hematoma and cytotoxic edema effects were acute; longer treatment windows may be possible for vasogenic edema. Our findings provide new insights to inform interpretation of ongoing trials as well as precision design (dose, sample size estimation, patient selection, outcome selection, and Bayesian analysis) of future TBI trials of glibenclamide.
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Critical care medicine · Feb 2023
Observational StudyProspective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.
To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients. ⋯ A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.