Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers.

ABSTRACT:

Purpose

To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.

Patients and methods

This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating chara

SUBMITTER: Fan R 

PROVIDER: S-EPMC9942777 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature