Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 17, 2025
Date Accepted: Mar 17, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting based on machine learning: Cohort Study
ABSTRACT
Background:
Ischemic heart disease remains the leading cause of death worldwide, coronary artery bypass grafting (CABG) remains the primary surgical treatment. A series of prediction models for operative mortality risk applicable to CABG have been established, but several problems still exist in poor discrimination, accuracy and applicability.
Objective:
To develop an innovative mortality risk prediction system for patients undergoing CABG based on multiple machine learning algorithms.
Methods:
From January 2017 to December 2020, patients underwent CABG in the Chinese Cardiac Surgery Registry (CCSR) were included. We developed and tested machine learning models to predict in-hospital mortality risk, comparing them to the EuroSCORE Ⅱ. End point was in-hospital mortality.
Results:
A total of 21443 patients were included. Overall, in-hospital mortality was 2.1%. extreme gradient boosting (XGBoost) model has the best discrimination and calibration in both training and test cohorts which were superior to the EuroSCORE Ⅱ.
Conclusions:
XGBoost prediction model can predict in-hospital mortality risk for patients after CABG in China.
Citation
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