Background: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to determine the theoretical performance of ML in augmenting AKI recognition. Methods: We developed ML models using the k-nearest neighbor (k-NN) algorithm. The ML models were trained-tested with clinical laboratory data for 50 adult burn patients that had neutrophil gelatinase associated lipocalin (NGAL), urine output (UOP), creatinine, and N-terminal B-type natriuretic peptide (NT-proBNP) measured within the first 24 h of admission. Results: Half of patients (50%) in the dataset experienced AKI within the first week following admission. ML models containing NGAL, creatinine, UOP, and NT-proBNP achieved 90–100% accuracy for identifying AKI. ML models containing only NT-proBNP and creatinine achieved 80–90% accuracy. Mean time-to-AKI recognition using UOP and/or creatinine alone was achieved within 42.7 ± 23.2 h post-admission vs. within 18.8 ± 8.1 h via the ML-algorithm. Conclusions: The performance of UOP and creatinine for predicting AKI could be enhanced by with a ML algorithm using a k-NN approach when NGAL is not available. Additional studies are needed to verify performance of ML for burn-related AKI.
- k-Nearest neighbor
- Neutrophil gelatinase associated lipocalin
- Urine output
ASJC Scopus subject areas
- Emergency Medicine
- Critical Care and Intensive Care Medicine