Development of Inpatient Risk Stratification Models of Acute Kidney Injury for Use in Electronic Health Records

Michael E. Matheny, Randolph A. Miller, T. Alp Ikizler, Lemuel R. Waitman, Joshua C. Denny, Jonathan S. Schildcrout, Robert S. Dittus and Josh F. Peterson
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Michael E. Matheny: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville, Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, michael.matheny@vanderbilt.edu
Randolph A. Miller: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville
Lemuel R. Waitman: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville
Joshua C. Denny: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Division of Nephrology, Vanderbilt University School of Medicine, Nashville, Division of General Internal Medicine, Vanderbilt University School of Medicine, Nashville
Robert S. Dittus: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville
Josh F. Peterson: Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, Division of General Internal Medicine and Public Health, Vanderbilt University School of Medicine, Nashville, Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville

Medical Decision Making, 2010, vol. 30, issue 6, 639-650

Abstract: Objective. Patients with hospital-acquired acute kidney injury (AKI) are at risk for increased mortality and further medical complications. Evaluating these patients with a prediction tool easily implemented within an electronic health record (EHR) would identify high-risk patients prior to the development of AKI and could prevent iatrogenically induced episodes of AKI and improve clinical management. Methods. The authors used structured clinical data acquired from an EHR to identify patients with normal kidney function for admissions from 1 August 1999 to 31 July 2003. Using administrative, computerized provider order entry and laboratory test data, they developed a 3-level risk stratification model to predict each of 2 severity levels of in-hospital AKI as defined by RIFLE criteria. The severity levels were defined as 150% or 200% of baseline serum creatinine. Model discrimination and calibration were evaluated using 10-fold cross-validation. Results. Cross-validation of the models resulted in area under the receiver operating characteristic (AUC) curves of 0.75 (150% elevation) and 0.78 (200% elevation). Both models were adequately calibrated as measured by the Hosmer-Lemeshow goodness-of-fit test chi-squared values of 9.7 (P = 0.29) and 12.7 (P = 0.12), respectively. Conclusions. The authors generated risk prediction models for hospital-acquired AKI using only commonly available electronic data. The models identify patients at high risk for AKI who might benefit from early intervention or increased monitoring.

Keywords: clinical prediction rules; decision rules; pharmacoepidemiology; risk adjustment; risk stratification; artificial neural networks. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:30:y:2010:i:6:p:639-650

DOI: 10.1177/0272989X10364246

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