Machine learning approach to predict acute kidney injury among patients undergoing multi-level spinal posterior instrumented fusion

Acute kidney injury (AKI) after spinal fusion is a significant morbidity that can lead to poor post-surgical outcomes. Identifying AKI risk factors and developing a risk model can raise surgeons' awareness and allow them to take actions to mitigate the risks. The objective of the current study is to develop machine learning (ML) models to assess patient risk factors predisposing to AKI after posterior spinal instrumented fusion.