Predicting Acute Kidney Injury with Nephrotoxic Burden in Non-Critical Patients

Background: Acute kidney injury (AKI) is an abrupt decline in kidney function that occurs in about 20% of hospitalized admissions and may lead to irreversible kidney damage. Methods: We developed and externally validated deep learning models to dynamically predict progression to Stage 2 or higher AKI defined by Kidney Disease: Improving Global Outcomes serum creatinine criteria within the next 48 hours. We used an extensive set of predictors including demographics, admission source, comorbidities, medications, laboratory and vitals measurements. Results: Our retrospective study includes adult non-critical care patients at the University of Pittsburgh Medical Center (UPMC) (2018-2022; n=39,755) and the University of Florida Health (UFH) (2012-2019; n=122,324). In the UFH and UPMC development cohort, the mean age was 55 and 71 and 55% (n=47,350) and 54% (n=15,128) were female, respectively. Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating characteristic curve values with 95% confidence interval (CI) ranged between 0.77 (95% CI 0.75-0.78) (UPMC Model-model trained on UPMC patients) and 0.81 (95% CI 0.79-0.82) (UFH Model-model trained on UF patients) for the UFH test cohort, and between 0.79 (95% CI 0.78,-0.8) (UFH Model) and 0.83 (95% CI 0.82-0.84) (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% CI 0.80-0.83) for UFH and 0.82 (95% CI 0.81-0.84]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the features with the highest influence across the models and institutions. Conclusions: The model developed using multi-center data had robust performance, suggesting implementation could help prevent AKI progression.