Predicting Risk for Acute Kidney Injury in the Outpatient Setting: a Continuous Risk Prediction Equation and Two Binary Tests for Identifying High-risk Patients

Abstract

Background: Risk-factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk-factors for AKI occurring and managed in the outpatient setting are unknown and may differ. Methods: A development cohort for modelling risk of AKI without concurrent or subsequent hospitalization was defined by repeated primary care encounters in a single urban healthcare system using electronic health record data. An external validation cohort was similarly defined in the Veterans Health Administration nationally. Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used to develop a model for outpatient AKI over an 18-month outcome period. The model was then transformed into two binary tests: one identifying high-risk subjects for potential research and another identifying patients for additional clinical monitoring or intervention. Results: Outpatient AKI was seen in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model produced C-statistics of 0.717 (95% confidence interval (CI): 0.710-0.725) and 0.722 (95% CI: 0.720-0.723) in the development and validation cohorts, respectively. The research-test, identifying the top 5.2% most at-risk patients in the validation cohort, had sensitivity of 0.210 (95% CI: 0.208-0.213) and specificity of 0.952 (95% CI: 0.951-0.952). The clinical-test, identifying the top 20% most at-risk, had sensitivity of 0.494 (95% CI: 0.491-0.497) and specificity of 0.806 (95% CI: 0.806-0.807). Conclusions: The outpatient AKI-risk prediction model performed well in both the development and validation cohorts and was transformed into two binary tests, one for potential use in research and another in clinical care. Multiple novel risk-factors were identified.