National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury. (23rd June 2015)

• Record Type: Journal Article
• Authors: Cronin, Robert M; VanHouten, Jacob P; Siew, Edward D; Eden, Svetlana K; Fihn, Stephan D; Nielson, Christopher D; Peterson, Josh F; Baker, Clifton R; Ikizler, T Alp; Speroff, Theodore; Matheny, Michael E
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 5(2015:Sep.)
• Page Start: 1054
• Page End: 1071
• Publication Date: 2015-06-23
• Subjects: risk models -- random forest -- logistic regression -- acute kidney injury; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv051
• Languages: English
• ISSNs: 1067-5027

Abstract

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. Materials and 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.