Optimizing predictive strategies for acute kidney injury after major vascular surgery.

ABSTRACT:

Background

Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery.

Methods

A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used

SUBMITTER: Filiberto AC 

PROVIDER: S-EPMC8276529 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature