Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery.

ABSTRACT:

Background

Acute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.

Methods

We included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the per

SUBMITTER: Li J 

PROVIDER: S-EPMC8801502 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature