Acute kidney injury prediction models: current concepts and future strategies. Issue 6 (November 2019)

• Record Type: Journal Article
• Authors: Park, Sehoon; Lee, Hajeong
• Is Part Of: Current opinion in nephrology and hypertension. Volume 28:Issue 6(2019)
• Publication Date: 2019-11
• Subjects: acute kidney injury -- artificial intelligence -- machine-learning -- model -- outcome; Hypertension -- Periodicals; Nephrology -- Periodicals; Hypertension -- Indexes; Hypertension -- Periodicals; Kidney Diseases -- Indexes; Kidney Diseases -- Periodicals; Nephrology -- Periodicals; 616.132
• DOI: 10.1097/MNH.0000000000000536
• Languages: English
• ISSNs: 1062-4821

Abstract

Abstract : Purpose of review: Acute kidney injury (AKI) is a critical condition associated with poor patient outcomes. We aimed to review the current concepts and future strategies regarding AKI risk prediction models. Recent findings: Recent studies have shown that AKI occurs frequently in patients with common risk factors and certain medical conditions. Prediction models for AKI risk have been reported in medical fields such as critical care medicine, surgery, nephrotoxic agent exposure, and others. However, practical, generalizable, externally validated, and robust AKI prediction models remain relatively rare. Further efforts to develop AKI prediction models based on comprehensive clinical data, artificial intelligence, improved delivery of care, and novel biomarkers may help improve patient outcomes through precise AKI risk prediction. Summary: This brief review provides insights for current concepts for AKI prediction model development. In addition, by overviewing the recent AKI prediction models in various medical fields, future strategies to construct advanced AKI prediction models are suggested.