Objective
Historically, acute kidney injury (AKI) carried a deadly prognosis in the burn population. Our aim with this study was to provide a modern description of AKI in the burn population and to develop a prediction tool for identifying patients at risk for late AKI.
Methods
A large multi-institution database, the Glue Grant's trauma related database (TRDB), was used to characterize AKI in a cohort of critically ill burn patients. We defined AKI according to the RIFLE criteria and categorized AKI as early, late or progressive. We then used Classification and Regression Tree (CART) analysis to create a decision tree with data obtained from the first 48 hours of admission to predict which subset of patients would develop late AKI. We tested the accuracy of this decision tree in a separate, single-institution cohort of burn patients who met the same criteria for entry into the Glue Grant study
Results
Of the 220 total patients analyzed from the Glue Grant cohort, 49 (22.2%) developed early AKI, 39 (17.7%) developed late AKI, and 16 (7.2%) developed progressive AKI. The group with progressive AKI was statistically older, with more comorbidities, and with the worst survival when compared to those with early or late AKI. Using CART analysis, we developed a decision tree with an overall accuracy of 80% for the development of late AKI for the Glue Grant dataset. We then tested this decision tree on a smaller dataset from our own institution to validate this tool, and found it to be 73% accurate.
Conclusions
AKI is common in severe burns with notable differences between early, late, and progressive AKI. Additionally, CART analysis provided a predictive model for early identification of patients at highest risk for developing late AKI with proven clinical accuracy.