Critical care medicine
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Critically ill patients are at high risk of acute brain injury. Bedside multimodality neuromonitoring techniques can provide a direct assessment of physiologic interactions between systemic derangements and intracranial processes and offer the potential for early detection of neurologic deterioration before clinically manifest signs occur. Neuromonitoring provides measurable parameters of new or evolving brain injury that can be used as a target for investigating various therapeutic interventions, monitoring treatment responses, and testing clinical paradigms that could reduce secondary brain injury and improve clinical outcomes. Further investigations may also reveal neuromonitoring markers that can assist in neuroprognostication. We provide an up-to-date summary of clinical applications, risks, benefits, and challenges of various invasive and noninvasive neuromonitoring modalities. ⋯ Neuromonitoring techniques provide an essential tool to facilitate early detection and treatment of acute brain injury in critical care. Awareness of the nuances of their use and clinical applications can empower the intensive care team with tools to potentially reduce the burden of neurologic morbidity in critically ill patients.
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Critical care medicine · Apr 2023
Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.
To predict impending delirium in ICU patients using recurrent deep learning. ⋯ Our delirium prediction model achieved strong performance by applying deep learning to a dataset that is at least one order of magnitude larger than those used in previous studies. Another novel aspect of our study is the temporal nature of our features and predictions. Our model enables accurate prediction of impending delirium in the ICU, which can potentially lead to early intervention, more efficient allocation of ICU resources, and improved patient outcomes.
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Critical care medicine · Apr 2023
Observational StudyTime to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest.
Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. ⋯ Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.
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Critical care medicine · Apr 2023
Observational StudyAge Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study.
A high body mass index (BMI) is associated with an unfavorable disease course in COVID-19, but not among those who require admission to the ICU. This has not been examined across different age groups. We examined whether age modifies the association between BMI and mortality among critically ill COVID-19 patients. ⋯ A higher BMI may be favorably associated with a lower mortality among those less than 45 years old. This is in line with the so-called "obesity paradox" that was established for other groups of critically ill patients in broad age ranges. Further research is needed to understand this favorable association in young critically ill patients with COVID-19.
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Critical care medicine · Apr 2023
Cardiovascular Subphenotypes in Acute Respiratory Distress Syndrome.
To use clustering methods on transthoracic echocardiography (TTE) findings and hemodynamic parameters to characterize circulatory failure subphenotypes and potentially elucidate underlying mechanisms in patients with acute respiratory distress syndrome (ARDS) and to describe their association with mortality compared with current definitions of right ventricular dysfunction (RVD). ⋯ LCA of TTE parameters identified four cardiovascular subphenotypes in ARDS that more closely aligned with circulatory failure mechanisms and mortality than current RVD definitions.