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Risk factor identification and prediction of pleural effusion following coronary artery bypass grafting
Caiyun Lu1,2, Fan Jiang3, Ling Pan2
1Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University Chaoyang, Beijing 100020, China.
Insights
A high incidence of pleural effusion (PE) occurs after coronary artery bypass grafting (CABG). A back-propagation neural network model effectively predicts PE risk, aiding early detection and patient management.
Area of Science:
- Cardiovascular Surgery
- Thoracic Medicine
- Medical Informatics
Background:
- Pleural effusion (PE) is a common complication following coronary artery bypass grafting (CABG).
- Identifying risk factors and developing predictive tools for PE post-CABG is crucial for patient care.
Purpose of the Study:
- To determine the incidence of PE after CABG.
- To identify independent risk factors associated with post-CABG PE.
- To develop and validate a predictive model for early PE detection.
Main Methods:
- Retrospective analysis of 1,979 patients undergoing CABG, split into training and validation sets.
- Risk factor identification using univariate, LASSO, and multivariate logistic regression.
- Development and comparison of five machine learning models, including a back-propagation neural network (BPNN), with external validation.
Main Results:
- PE incidence was 71.0% within 3 days post-CABG.
- Independent risk factors included BMI, carotid artery stenosis, postoperative pneumonia, ventilation duration, blood loss, operative time, and ejection fraction.
- The BPNN model achieved the highest predictive performance (AUC 0.828 training, 0.751 internal validation, 0.737 external validation).
Conclusions:
- A validated predictive model for post-CABG PE was successfully developed.
- The BPNN model demonstrates strong discriminatory power for early risk stratification.
- This tool can assist clinicians in identifying high-risk patients for timely intervention.
Objective:
To evaluate the incidence of pleural effusion (PE) following coronary artery bypass grafting (CABG), identify associated risk factors, and develop a validated predictive model for early detection.
Methods:
A retrospective cohort of 1,979 patients who underwent CABG at Beijing Chaoyang Hospital (Capital Medical University) was randomly divided into training (70%) and validation (30%) sets. Risk factors for PE were identified through univariate analysis, LASSO regression, and multivariate logistic regression. Five machine learning models-nomogram, back-propagation neural network (BPNN), random forest, gradient boosting, and support vector machine-were developed. External validation was performed using data from 289 patients at the First Affiliated Hospital of Guangxi Medical University.
Results:
PE occurred in 71.0% of patients (1,405/1,979) within 3 days postoperatively. Independent risk factors included body mass index (BMI), carotid artery stenosis, postoperative pneumonia, duration of mechanical ventilation, intraoperative blood loss, operative time, and ejection fraction. Among the models, the BPNN demonstrated the best performance, with area under the curve (AUC) values of 0.828 in the training set and 0.751 in the internal validation set. The AUC for external validation was 0.737, outperforming the other models across all evaluation metrics.
Conclusions:
This study developed a predictive model for post-CABG pleural effusion with high discriminatory power, providing a useful tool for early risk stratification in clinical settings.
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