Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores.

ABSTRACT:

Objective

Current cardiac surgery risk models do not address a substantial fraction of procedures. We sought to create models to predict the risk of operative mortality for an expanded set of cases.

Methods

Four supervised machine learning models were trained using preoperative variables present in the Society of Thoracic Surgeons (STS) data set of the Massachusetts General Hospital to predict and classify operative mortality in procedures without STS risk scores. A total of 424 (5.5%) mortality events occurred out of 7745 cases. Models included logistic regression with elastic net regularization (LogReg), support vector machine, random forest (RF), and extreme gradient boosted trees (XGBoost). Model discrimination was assessed via area under the receiver operating character

SUBMITTER: Ong CS 

PROVIDER: S-EPMC8918430 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature