Abstract
BACKGROUND: Likelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR). METHODS: We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models. RESULTS: In our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18-35 (7.29%), 36-55 (8.82%), 56-65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18-35, 36-55, 56-65, and > 65 were 0.784 (95% CI, 0.769-0.800), 0.766 (95% CI, 0.754-0.777), 0.754 (95% CI, 0.741-0.768), and 0.725 (95% CI, 0.709-0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups. CONCLUSIONS: As complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.