title: National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury.
abstract: OBJECTIVE
  Hospital-acquired acute kidney injury (HA-AKI) is a potentially preventable cause of morbidity and mortality. Identifying high-risk patients prior to the onset of kidney injury is a key step towards AKI prevention.
  
  
  METHODS
  A national retrospective cohort of 1,620,898 patient hospitalizations from 116 Veterans Affairs hospitals was assembled from electronic health record (EHR) data collected from 2003 to 2012. HA-AKI was defined at stage 1+, stage 2+, and dialysis. EHR-based predictors were identified through logistic regression, least absolute shrinkage and selection operator (lasso) regression, and random forests, and pair-wise comparisons between each were made. Calibration and discrimination metrics were calculated using 50 bootstrap iterations. In the final models, we report odds ratios, 95% confidence intervals, and importance rankings for predictor variables to evaluate their significance.
  
  
  RESULTS
  The area under the receiver operating characteristic curve (AUC) for the different model outcomes ranged from 0.746 to 0.758 in stage 1+, 0.714 to 0.720 in stage 2+, and 0.823 to 0.825 in dialysis. Logistic regression had the best AUC in stage 1+ and dialysis. Random forests had the best AUC in stage 2+ but the least favorable calibration plots. Multiple risk factors were significant in our models, including some nonsteroidal anti-inflammatory drugs, blood pressure medications, antibiotics, and intravenous fluids given during the first 48 h of admission.
  
  
  CONCLUSIONS
  This study demonstrated that, although all the models tested had good discrimination, performance characteristics varied between methods, and the random forests models did not calibrate as well as the lasso or logistic regression models. In addition, novel modifiable risk factors were explored and found to be significant.
authors.affiliation: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Robert M
authors.initials: RM
authors.lastname: Cronin
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Jacob P
authors.initials: JP
authors.lastname: VanHouten
authors.affiliation: Division of Nephrology, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Edward D
authors.initials: ED
authors.lastname: Siew
authors.affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Svetlana K
authors.initials: SK
authors.lastname: Eden
authors.affiliation: Office of Analytics and Business Intelligence, VA Central Office, Veterans Health Administration, Seattle, WA, USA Division of General Internal Medicine, University of Washington, Seattle, WA, USA.
authors.forename: Stephan D
authors.initials: SD
authors.lastname: Fihn
authors.affiliation: Office of Analytics and Business Intelligence, VA Central Office, Veterans Health Administration, Seattle, WA, USA Division of Pulmonary Medicine and Critical Care, University of Nevada, Reno, NV, USA.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Nielson
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Josh F
authors.initials: JF
authors.lastname: Peterson
authors.affiliation: Office of Analytics and Business Intelligence, VA Central Office, Veterans Health Administration, Seattle, WA, USA.
authors.forename: Clifton R
authors.initials: CR
authors.lastname: Baker
authors.affiliation: Division of Nephrology, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: T Alp
authors.initials: TA
authors.lastname: Ikizler
authors.affiliation: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, USA Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville, TN, USA Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Theodore
authors.initials: T
authors.lastname: Speroff
authors.affiliation: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville, TN, USA Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA Michael.matheny@vanderbilt.edu.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Matheny
mesh_terms: D058186:Acute Kidney Injury; D000368:Aged; D005260:Female; D006760:Hospitalization; D006787:Hospitals, Veterans; D006801:Humans; D007049:Iatrogenic Disease; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D012306:Risk; D014481:United States; D014493:United States Department of Veterans Affairs
keywords: acute kidney injury; logistic regression; random forest; risk models