Electronic Health Record-Based Predictive Models for Acute Kidney Injury Screening in Pediatric Inpatients

BACKGROUND: Acute kidney injury (AKI) is common in pediatric inpatients and associated with increased morbidity, mortality, and length of stay. Early identification can reduce severity. METHODS: To create and validate an electronic health record (EHR)-based AKI screening tool, we generated temporally distinct development and validation cohorts using retrospective data from our tertiary care children’s hospital, including children 28 days through 21 years old with sufficient serum creatinine measurements to determine AKI status. AKI was defined as 1.5-fold or 0.3 mg/dL increase in serum creatinine. Age, medication exposures, platelet count, red blood cell distribution width, serum phosphorus, serum transaminases, hypotension (ICU only), and pH (ICU only) were included in AKI risk prediction models. RESULTS: For ICU patients, 791/1332 (59%) of the development cohort and 470/866 (54%) of the validation cohort had AKI. In external validation, the ICU prediction model had C-statistic=0.74 (95% confidence interval 0.71–0.77). For non-ICU patients, 722/2337 (31%) and 469/1474 (32%) had AKI, and the prediction model had C-statistic=0.69 (0.66–0.72). CONCLUSIONS: AKI screening can be performed using EHR data. The AKI screening tool can be incorporated into EHR systems to identify high risk patients without serum creatinine data, enabling targeted laboratory testing, early AKI identification, and modification of care.