Development of inpatient risk stratification models of acute kidney injury for use in electronic health records.
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.
Matheny
Michael E
ME
Geriatric Research Education Clinical Center, Tennessee Valley Health System, Veterans Health Administration, Nashville, TN, USA. michael.matheny@vanderbilt.edu
Miller
Randolph A
RA
Ikizler
T Alp
TA
Waitman
Lemuel R
LR
Denny
Joshua C
JC
Schildcrout
Jonathan S
JS
Dittus
Robert S
RS
Peterson
Josh F
JF
D058186:Acute Kidney Injury / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology*; D000293:Adolescent; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D016001:Confidence Intervals; D020000:Decision Support Systems, Clinical / Q000295:instrumentation*; D003661:Decision Support Techniques; D003953:Diagnosis-Related Groups; D018450:Disease Progression; D005260:Female; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000295:instrumentation*; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016017:Odds Ratio; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013714:Tennessee / Q000453:epidemiology; D055815:Young Adult