Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery.

ABSTRACT:

Importance

Early recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries.

Objective

To develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery.

Design setting and participants

ML models, referred as Roux-MMC model, were developed and validated using a retrospective cohort extracted from the STS Adult Cardiac Surgery Database (ACSD) at Maine Medical Center (MMC) between January 2012 to December 2021. It was further validated on a prospective cohort of MMC between January 2022 to February 2024. The performance of Roux-MMC model is compared with the

SUBMITTER: Jin Q 

PROVIDER: S-EPMC11888533 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature