Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.

Journal: Critical care (London, England)

Published Date: Apr 8, 2019

Abstract

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 volume-responsive (VR) and volume-unresponsive (VU) AKI.

Authors

Zhongheng Zhang

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Kwok M Ho

School of Population and Global Health, University of Western Australia, Perth, Australia.
Yucai Hong

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.

Keywords

Acute Kidney Injury Aged Aged, 80 and over Critical Care Female Fluid Therapy Humans Intensive Care Units Logistic Models Machine Learning Male Middle Aged Oliguria Time Factors

External Resources

View on PubMed Access via DOI PubMed (30961662)

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.

Abstract

Authors

Keywords

External Resources

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