Seminars in respiratory and critical care medicine
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Arterial pressure management is a crucial task in the operating room and intensive care unit. In high-risk surgical and in critically ill patients, sustained hypotension is managed with continuous infusion of vasopressor agents, which most commonly have direct α agonist activity like phenylephrine or norepinephrine. ⋯ This approach may be improved by using automated systems that titrate vasopressor infusions to maintain a target pressure. In this article, we review the evidence behind blood pressure management in the operating room and intensive care unit and discuss current and potential future applications of automated blood pressure control.
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Traumatic brain injury (TBI) is the leading cause of death and disability in trauma patients, and can be classified into mild, moderate, and severe by the Glasgow coma scale (GCS). Prehospital, initial emergency department, and subsequent intensive care unit (ICU) management of severe TBI should focus on avoiding secondary brain injury from hypotension and hypoxia, with appropriate reversal of anticoagulation and surgical evacuation of mass lesions as indicated. Utilizing principles based on the Monro-Kellie doctrine and cerebral perfusion pressure (CPP), a surrogate for cerebral blood flow (CBF) should be maintained by optimizing mean arterial pressure (MAP), through fluids and vasopressors, and/or decreasing intracranial pressure (ICP), through bedside maneuvers, sedation, hyperosmolar therapy, cerebrospinal fluid (CSF) drainage, and, in refractory cases, barbiturate coma or decompressive craniectomy (DC). ⋯ Optimization of the acute care of severe TBI should include recognition and treatment of paroxysmal sympathetic hyperactivity (PSH), early seizure prophylaxis, venous thromboembolism (VTE) prophylaxis, and nutrition optimization. Despite this, severe TBI remains a devastating injury and palliative care principles should be applied early. To better affect the challenging long-term outcomes of severe TBI, more and continued high quality research is required.
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The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. ⋯ Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers.