Model to Predict Mortality in Critically Ill Adults with Acute Kidney Injury-Reference-Cited by-同舟云学术

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Model to Predict Mortality in Critically Ill Adults with Acute Kidney Injury

Published:2011-09 Issue:9 Volume:6 Page:2114-2120
ISSN:1555-9041
Container-title:Clinical Journal of the American Society of Nephrology
language:en
Short-container-title:CJASN

Author:

Demirjian Sevag,Chertow Glenn M.,Zhang Jane Hongyuan,O'Connor Theresa Z.,Vitale Joseph,Paganini Emil P.,Palevsky Paul M.,

Publisher

American Society of Nephrology (ASN)

Subject

Transplantation,Nephrology,Critical Care and Intensive Care Medicine,Epidemiology

Reference24 articles.

1. Acute Renal Failure in Critically Ill PatientsA Multinational, Multicenter Study

2. Intensity of renal replacement therapy in acute kidney injury: perspective from within the Acute Renal Failure Trial Network Study

3. Predictors of Health Utility among 60-Day Survivors of Acute Kidney Injury in the Veterans Affairs/National Institutes of Health Acute Renal Failure Trial Network Study

4. Continuous versus intermittent renal replacement therapy for critically ill patients with acute kidney injury: A meta-analysis*

5. Intensity of Renal Support in Critically Ill Patients with Acute Kidney Injury

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1. Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury;BMC Medical Informatics and Decision Making;2024-07-25

2. Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy;Journal of International Medical Research;2024-03

3. The Impact of C-Reactive Protein-To-Albumin Ratio on Mortality in Patients with Acute Kidney Injury Requiring Continuous Renal Replacement Therapy: A Multicenter Retrospective Study;Nephron;2023-11-29

4. Development and validation of a nomogram to predict the risk of renal replacement therapy among acute kidney injury patients in intensive care unit;Clinical and Experimental Nephrology;2023-07-27

5. Predicting outcomes of acute kidney injury in critically ill patients using machine learning;Scientific Reports;2023-06-18

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