Development and validation of an automated algorithm for identifying patients at higher risk for drug-induced acute kidney injury

Purpose. Using information from institutional electronic health records, we aimed to develop dynamic predictive models to identify patients at high risk of acute kidney injury (AKI) among those who received a nephrotoxic medication during their hospital stay. Methods. Candidate predictors were measured for each of the first 5 hospital days where a patient received a nephrotoxic medication (risk model days) to predict an AKI, using logistic regression with reduced backward variables elimination in 100 bootstrap samples. An AKI event was defined as an increase of serum creatinine >= 200% of a baseline SCr within 5 days after a risk model day. Final models were internally validated by replication in 100 bootstrap samples and a risk score for each patient was calculated from the validated model. As performance measures, the area under the receiver operation characteristic curves (AUC) and the number of AKI events among patients who had high risk scores were estimated. Results. The study population included 62,561 admissions followed by 1,212 AKI events (1.9 events/100 admissions). We constructed 5 risk models corresponding to the first 5 hospital days where patients were exposed to at least one nephrotoxic medication. Validated AUCs of the 5 models ranged from 0.78 to 0.81. Depending on risk model day, admissions ranked in the 90th percentile of the risk score captured between 43% to 49% of all AKI events. Conclusion. A dynamic prediction model was built successfully for inpatient AKI with excellent discriminative validity and good calibration, allowing clinicians to focus on a select high-risk population that captures the majority of AKI events.