Creating a Machine Learning Tool to Predict Acute Kidney Injury in African American Hospitalized Patients

Abstract

Machine learning (ML) has been used to build high-performance prediction models in the past without considering race. African Americans (AA) are vulnerable to acute kidney injury (AKI) at a higher eGFR level than Caucasians. AKI increases mortality, length of hospital stays, and incidence of chronic kidney disease (CKD) and end-stage renal disease (ESRD). We aimed to establish an ML-based prediction model for the early identification of AKI in hospitalized AA patients by utilizing patient-specific factors in an ML algorithm to create a predictor tool. This is a single-center, retrospective chart review. We included participants 18 years or older and admitted to an urban academic medical center. Two hundred participants were included in the study. Our ML training set provided a result of 77% accuracy for the prediction of AKI given the attributes collected. For the test set, AKI was accurately predicted in 71% of participants. The clinical significance of this model can lead to great advancements in the care of AA patients and provide practitioners avenues to optimize their therapy of choice in AAs when given AKI risk ahead of time.

Keywords

• acute kidney injury
• African Americans
• machine learning