Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.

ABSTRACT:

Background

Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative time points.

Methods

The Society of Thoracic Surgeons (STS) registry data elements from 2086 isolated CABG patients were divided into training and testing datasets and input into Extreme Gradient Boosting decision-tree machine learning algorithms. Two prediction models were developed based on data from preoperative (80 parameters) and postoperative (125 parameters) phases of care. Outcomes included operative mortality, major morbidity or mortality, high cost, and 30-day readmission. Machine learning and STS model performance were as

SUBMITTER: Zea-Vera R 

PROVIDER: S-EPMC9703607 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature