title: Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.
abstract: The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.
authors.affiliation: Division of Nephrology, Department of Pediatrics, Stanford University, 300 Pasteur Drive, Room G-306, Stanford, CA 94304 USA.
authors.forename: Scott M
authors.initials: SM
authors.lastname: Sutherland
authors.affiliation: Departments of Medicine and Critical Care, George Washington University Medical Center, Washington, DC USA.
authors.forename: Lakhmir S
authors.initials: LS
authors.lastname: Chawla
authors.affiliation: Departments of Pharmacy, Critical Care Medicine and Clinical Translational Sciences, University of Pittsburgh, Pittsburgh, PA USA.
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Kane-Gill
authors.affiliation: Department of Medicine, Division of Nephrology, University of California San Francisco, San Francisco, CA USA.
authors.forename: Raymond K
authors.initials: RK
authors.lastname: Hsu
authors.affiliation: Prescient Healthcare Consulting, LLC, Charlottesville, VA USA.
authors.forename: Andrew A
authors.initials: AA
authors.lastname: Kramer
authors.affiliation: Division of Pediatric Nephrology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA.
authors.forename: Stuart L
authors.initials: SL
authors.lastname: Goldstein
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Kellum
authors.affiliation: Department of Nephrology, Dialysis and Transplantation, International Renal Research Institute of Vicenza, San Bortolo Hospital, Vicenza, Italy.
authors.forename: Claudio
authors.initials: C
authors.lastname: Ronco
authors.affiliation: Division of Critical Care, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.
authors.forename: Sean M
authors.initials: SM
authors.lastname: Bagshaw
keywords: Acute kidney injury; Big data; Electronic health record; Electronic medical record; Prediction; Predictive analytics