Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.

ABSTRACT:

Importance

Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.

Objective

To internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.

Design, setting, and participants

This diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission grea

SUBMITTER: Churpek MM 

PROVIDER: S-EPMC7420241 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature