Current opinion in critical care
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Curr Opin Crit Care · Dec 2020
ReviewUses and pitfalls of measurement of end-tidal carbon dioxide during cardiac arrest.
To discuss recent studies relevant to the utility of measuring end-tidal carbon dioxide (ETCO2) during cardiopulmonary resuscitation (CPR) and its correlation with outcome in adults experiencing cardiac arrest. ⋯ Higher values of ETCO2 during resuscitation from cardiac arrest are generally associated with a greater likelihood of ROSC. However, timing of measurements and cut-off values used show significant variability across different studies, making it hard to draw any conclusions about the utility of any particular reading for prognostication. Future studies might aim to develop an accepted standard for the timing and cut-off value of ETCO2 used, to enable comparison of the parameter across different studies.
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Curr Opin Crit Care · Dec 2020
ReviewReal-time glomerular filtration rate: improving sensitivity, accuracy and prognostic value in acute kidney injury.
Acute kidney injury (AKI) is common and associated with high patient mortality, and accelerated progression to chronic kidney disease. Our ability to diagnose and stratify patients with AKI is paramount for translational progress. Unfortunately, currently available methods have major pitfalls. Serum creatinine is an insensitive functional biomarker of AKI, slow to register the event and influenced by multiple variables. Cystatin C, a proposed alternative, requires long laboratory processing and also lacks specificity. Other techniques are either very cumbersome (inuline, iohexol) or involve administration of radioactive products, and are therefore, not applicable on a large scale. ⋯ The clinical utility of rapid GFR measurements in AKI patients remains unknown as these disruptive technologies have not been tested in studies exploring clinical outcomes. However, these approaches have the potential to improve our understanding of AKI and clinical care. This overdue technology has the potential to individualize patient care and foster therapeutic success in AKI.
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Curr Opin Crit Care · Dec 2020
ReviewArtificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.
Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisting comorbidities and the cause of the current disease. Besides, many other parameters affect the kidney function, such as the state of other vital organs, the host response, and the initiated treatment. Advancements in the field of informatics have led to the opportunity to store and utilize the patient-related data to train and validate models to detect specific patterns and, as such, predict disease states or outcomes. ⋯ In this article, we provide an overview of the machine-learning prediction models for AKI and its outcomes in critically ill patients and individuals undergoing major surgery. We also discuss the pitfalls and the opportunities related to the implementation of these models in clinical practices.
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The current narrative review discusses practical applications of stress and damage biomarkers for the management of acute kidney injury (AKI) based on clinical trials and real-world evaluations. ⋯ Stress and damage biomarker-based approaches to patient care seem to be promising for identifying patients at high risk for developing AKI and thus offers an opportunity for early management to prevent and ameliorate AKI and drug-associated AKI.
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AKI is a complex clinical syndrome with many causes and there is a broad range of clinical presentations that vary according to duration, severity and context. Established consensus definitions of AKI are nonspecific and limited to kidney function. This reduces treatment options to generic approaches rather than individualized, cause-based strategies that have limited both understanding and management of AKI. ⋯ Appropriate intervention requires refinement of the nomenclature of AKI to identify subphenotypes that facilitate correctly timed and selectively targeted intervention.