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

ABSTRACT:

Background

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.

Methods

Data was collected from the IBM MarketScan Database (2009-2021) for patients >18 years old who underwent spinal fusion with posterior instrumentation (3-6 levels). AKI incidence (defined by the International Classification of Diseases codes) was recorded 90-day post-surgery. Risk factors for AKI were investigated and compared throu

SUBMITTER: Heo KY 

PROVIDER: S-EPMC11467292 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature