L Nelson Sanchez-Pinto1, Robinder G Khemani. 1. 1Department of Pediatrics, Section of Critical Care, The University of Chicago, Chicago, IL. 2Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA.
Abstract
OBJECTIVE: Acute kidney injury is independently associated with poor outcomes in critically ill children. However, the main biomarker of acute kidney injury, serum creatinine, is a late marker of injury and can cause a delay in diagnosis. Our goal was to develop and validate a data-driven multivariable clinical prediction model of acute kidney injury in a general PICU using electronic health record data. DESIGN: Derivation and validation of a prediction model using retrospective data. PATIENTS: All patients 1 month to 21 years old admitted between May 2003 and March 2015 without acute kidney injury at admission and alive and in the ICU for at least 24 hours. SETTING: A multidisciplinary, tertiary PICU. INTERVENTION: The primary outcome was early acute kidney injury, which was defined as new acute kidney injury developed in the ICU within 72 hours of admission. Multivariable logistic regression was performed to derive the Pediatric Early AKI Risk Score using electronic health record data from the first 12 hours of ICU stay. MEASUREMENTS AND MAIN RESULTS: A total of 9,396 patients were included in the analysis, of whom 4% had early acute kidney injury, and these had significantly higher mortality than those without early acute kidney injury (26% vs 3.3%; p < 0.001). Thirty-three candidate variables were tested. The final model had seven predictors and had good discrimination (area under the curve 0.84) and appropriate calibration. The model was validated in two validation sets and maintained good discrimination (area under the curves, 0.81 and 0.86). CONCLUSION: We developed and validated the Pediatric Early AKI Risk Score, a data-driven acute kidney injury clinical prediction model that has good discrimination and calibration in a general PICU population using only electronic health record data that is objective, available in real time during the first 12 hours of ICU care and generalizable across PICUs. This prediction model was designed to be implemented in the form of an automated clinical decision support system and could be used to guide preventive, therapeutic, and research strategies.
OBJECTIVE:Acute kidney injury is independently associated with poor outcomes in critically ill children. However, the main biomarker of acute kidney injury, serum creatinine, is a late marker of injury and can cause a delay in diagnosis. Our goal was to develop and validate a data-driven multivariable clinical prediction model of acute kidney injury in a general PICU using electronic health record data. DESIGN: Derivation and validation of a prediction model using retrospective data. PATIENTS: All patients 1 month to 21 years old admitted between May 2003 and March 2015 without acute kidney injury at admission and alive and in the ICU for at least 24 hours. SETTING: A multidisciplinary, tertiary PICU. INTERVENTION: The primary outcome was early acute kidney injury, which was defined as new acute kidney injury developed in the ICU within 72 hours of admission. Multivariable logistic regression was performed to derive the Pediatric Early AKI Risk Score using electronic health record data from the first 12 hours of ICU stay. MEASUREMENTS AND MAIN RESULTS: A total of 9,396 patients were included in the analysis, of whom 4% had early acute kidney injury, and these had significantly higher mortality than those without early acute kidney injury (26% vs 3.3%; p < 0.001). Thirty-three candidate variables were tested. The final model had seven predictors and had good discrimination (area under the curve 0.84) and appropriate calibration. The model was validated in two validation sets and maintained good discrimination (area under the curves, 0.81 and 0.86). CONCLUSION: We developed and validated the Pediatric Early AKI Risk Score, a data-driven acute kidney injury clinical prediction model that has good discrimination and calibration in a general PICU population using only electronic health record data that is objective, available in real time during the first 12 hours of ICU care and generalizable across PICUs. This prediction model was designed to be implemented in the form of an automated clinical decision support system and could be used to guide preventive, therapeutic, and research strategies.
Authors: L Nelson Sanchez-Pinto; Laura Ruth Venable; John Fahrenbach; Matthew M Churpek Journal: Int J Med Inform Date: 2018-05-21 Impact factor: 4.046
Authors: Jeffrey S Upperman; John C Bucuvalas; Felicia N Williams; Bruce A Cairns; Charles S Cox; Allan Doctor; Robert F Tamburro Journal: Pediatr Crit Care Med Date: 2017-03 Impact factor: 3.624
Authors: Ibrahim Sandokji; Yu Yamamoto; Aditya Biswas; Tanima Arora; Ugochukwu Ugwuowo; Michael Simonov; Ishan Saran; Melissa Martin; Jeffrey M Testani; Sherry Mansour; Dennis G Moledina; Jason H Greenberg; F Perry Wilson Journal: J Am Soc Nephrol Date: 2020-05-07 Impact factor: 10.121
Authors: Li Wang; Tracy L McGregor; Deborah P Jones; Brian C Bridges; Geoffrey M Fleming; Jana Shirey-Rice; Michael F McLemore; Lixin Chen; Asli Weitkamp; Daniel W Byrne; Sara L Van Driest Journal: Pediatr Res Date: 2017-05-31 Impact factor: 3.756
Authors: Sara L Van Driest; Li Wang; Michael F McLemore; Brian C Bridges; Geoffrey M Fleming; Tracy L McGregor; Deborah P Jones; Jana Shirey-Rice; Cheryl L Gatto; James C Gay; Daniel W Byrne; Asli Weitkamp; Dan M Roden; Gordon Bernard Journal: Pediatr Res Date: 2019-08-27 Impact factor: 3.756
Authors: Lindsay P Zimmerman; Paul A Reyfman; Angela D R Smith; Zexian Zeng; Abel Kho; L Nelson Sanchez-Pinto; Yuan Luo Journal: BMC Med Inform Decis Mak Date: 2019-01-31 Impact factor: 3.298