AIMC Topic: Oliguria

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External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

Journal of nephrology May 12, 2022
OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.
Humans Acute Kidney Injury Critical Illness Intensive Care Units Deep Learning Oliguria
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Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.

Critical care (London, England) Apr 8, 2019
BACKGROUND AND OBJECTIVES: Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volum...
Humans Machine Learning Acute Kidney Injury Intensive Care Units Female Aged Aged, 80 and over Male Middle Aged Time Factors Logistic Models Fluid Therapy Critical Care Oliguria
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