Critical care : the official journal of the Critical Care Forum
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Multicenter Study Observational Study
Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest.
The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE). ⋯ GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.
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This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. ⋯ Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.
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Observational Study
Anti-inflammatory therapies are associated with delayed onset of anemia and reduction in transfusion requirements in critically ill patients: results from two studies.
Anemia is a hallmark of critical illness, which is largely inflammatory driven. We hypothesized that the use of anti-inflammatory agents limits the development of anemia and reduces the need for red blood cell (RBC) transfusions in patients with a hyper-inflammatory condition due to COVID-19. ⋯ Immunomodulatory treatment was associated with a slower decline in Hb level in critically ill patients with COVID-19 and with less transfusion. Findings point toward inflammation as an important cause for the occurrence of anemia in the critically ill.
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Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. ⋯ AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.