Related Experiment Video
Updated: Oct 30, 2025
Author Spotlight: Enhancing Coronary Artery Revascularization
Published on: September 15, 2023
Severe bleeding following off-pump coronary artery bypass grafting: predictive factors and risk model
Yu Liu1,2, Xing Wang3, Zi-Ying Chen2
1Department of Surgery, Hebei Medical University, Shijiazhuang, China.
Insights
This study developed a new model to predict severe bleeding after off-pump coronary artery bypass grafting (OPCABG). The model identifies key risk factors, aiding in better patient management for this cardiac surgery complication.
Area of Science:
- Cardiovascular Surgery
- Medical Predictive Modeling
- Patient Safety
Background:
- Severe bleeding is a significant complication after cardiac surgery, particularly off-pump coronary artery bypass grafting (OPCABG).
- Existing predictive models for severe bleeding post-OPCABG are limited, necessitating the development of new tools.
- This research addresses the need for a comprehensive risk assessment model for severe bleeding in isolated OPCABG patients.
Purpose of the Study:
- To identify independent predictors of severe bleeding after isolated OPCABG.
- To develop and validate a risk assessment model for predicting severe bleeding post-OPCABG.
- To improve patient outcomes by enabling early identification of high-risk individuals.
Main Methods:
- Retrospective analysis of clinical data from 584 patients undergoing OPCABG (January 2018 - April 2020).
- Multifactor logistic regression used to identify predictors of severe bleeding.
- Model performance evaluated using consistency index, calibration curves, decision curves, and ROC analysis.
Main Results:
- Eight independent risk factors for severe bleeding were identified: male sex, aspirin/clopidogrel withdrawal time, platelet count, fibrinogen level, C-reactive protein, serum creatinine, and total bilirubin.
- The predictive model demonstrated strong performance with a C-index of 0.859 in the training group and 0.807 in the validation group.
- Decision curve analysis confirmed the clinical utility of the model for both patient groups.
Conclusions:
- A novel risk assessment model effectively predicts severe bleeding following isolated OPCABG.
- The identified risk factors and the developed model warrant further investigation and clinical validation.
- This tool has the potential to enhance preoperative risk stratification and guide clinical decision-making in OPCABG surgery.
Background:
Severe bleeding following cardiac surgery remains a troublesome complication, but to date, there is a lack of comprehensive predictive models for the risk of severe bleeding following off-pump coronary artery bypass grafting (OPCABG). This study aims to analyze relevant indicators of severe bleeding after isolated OPCABG and establish a corresponding risk assessment model.
Methods:
The clinical data of 584 patients who underwent OPCABG from January 2018 to April 2020 were retrospectively analyzed. We gathered the preoperative baseline data and postoperative data immediately after intensive care unit admission and used multifactor logistic regression to screen the potential predictors of severe bleeding, upon which we established a predictive model. Using the consistency index and calibration curve, decision curve, and clinical impact curve analysis, we evaluated the performance of the model.
Results:
This study is the first to establish a risk assessment and prediction model for severe bleeding following isolated OPCABG. Eight independent risk factors were identified: male sex, aspirin/clopidogrel withdrawal time, platelet count, fibrinogen level, C-reactive protein, serum creatinine, and total bilirubin. Among the 483 patients in the training group, 138 patients (28.6%) had severe bleeding; among the 101 patients in the verification group, 25 patients (24.8%) had severe bleeding. Receiver operating characteristic (ROC) curve analysis for the internal training group revealed a convincing performance with a concordance index (C-index) of 0.859, while the area under the ROC curve for the external validation data was 0.807. Decision curve analysis showed that the model was useful for both groups.
Conclusions:
Although there are some limitations, the model can effectively predict the probability of severe bleeding following isolated OPCABG and is therefore worthy of further exploration and verification.
