Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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Multicenter Study
Predicting the Need for Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.
Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs. ⋯ We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24 h of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.
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Mitochondrial transplantation is a promising strategy for the treatment of several diseases. However, the effects of mitochondrial transplantation on the outcome of polymicrobial sepsis remain unclear. ⋯ These data displayed that mitochondrial replenishment reduces systemic inflammation and organ injury, enhances bacterial clearance, and improves the survival rate in sepsis. Thus, extraneous mitochondrial replenishment may be an effective adjunctive treatment to reduce sepsis-related mortality.
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Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time. ⋯ This study demonstrates that salient physiomarkers derived from continuous bedside monitoring are temporally and differentially expressed in septic patients. Using this information, minimalistic artificial intelligence models can be developed to predict sepsis earlier in critically ill patients.