A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children
CLINICAL RESEARCH www.jasn.org A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children Ibrahim Sandokji ,1,2 Yu Yamamoto ,2 Aditya Biswas,2 Tanima Arora ,2 Ugochukwu Ugwuowo,2 Michael Simonov,2 Ishan Saran,2 Melissa Martin ,2 Jeffrey M. Testani,3 Sherry Mansour,2 Dennis G. Moledina ,2 Jason H. Greenberg ,1,2 and F. Perry Wilson 2 1Department of Pediatrics, Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut 2Clinical and Translational Research Accelerator, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut 3Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut ABSTRACT Background Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. Methods We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external valida- tion cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour win- dow. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. Results Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively.
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