Development of inpatient risk stratification models of acute kidney injury for use in electronic health records.

Matheny ME, Miller RA, Ikizler TA, Waitman LR, Denny JC, Schildcrout JS, Dittus RS, Peterson JF
Med Decis Making. 2010 30 (6): 639-50

PMID: 20354229 · PMCID: PMC4850549 · DOI:10.1177/0272989X10364246

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.

MeSH Terms (28)

Acute Kidney Injury Adolescent Adult Aged Area Under Curve Confidence Intervals Decision Support Systems, Clinical Decision Support Techniques Diagnosis-Related Groups Disease Progression Female Humans Inpatients Length of Stay Logistic Models Male Medical Records Systems, Computerized Middle Aged Models, Statistical Neural Networks, Computer Odds Ratio Prognosis Retrospective Studies Risk Assessment Risk Factors ROC Curve Tennessee Young Adult

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