Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers
Lei Hua1, Jingxian Han1, Siqi Zhang1
1Henan Key Laboratory of Cardiac Remodeling and Transplantation, The 7th People's Hospital of Zhengzhou, Zhengzhou, Henan, P.R. China.
Insights
A new model predicts postoperative atrial fibrillation (POAF) risk after coronary artery bypass grafting (CABG) using clinical and genetic factors. This tool aids personalized perioperative care for better patient outcomes.
Area of Science:
- Cardiology
- Genetics
- Artificial Intelligence
Background:
- Postoperative atrial fibrillation (POAF) is a common complication after coronary artery bypass grafting (CABG).
- POAF significantly affects patient prognosis and increases healthcare costs.
- Accurate risk stratification is crucial for optimizing clinical management.
Purpose of the Study:
- To develop an integrated predictive model for POAF risk stratification.
- To optimize clinical management and personalized perioperative care for CABG patients.
Main Methods:
- Retrospective analysis of 576 CABG patients from a cohort of 2,528 undergoing 21-gene pharmacogenetic testing.
- Training and validation of eight machine learning algorithms using clinical variables and genetic variants.
- Independent validation on a separate cohort of 61 patients.
Main Results:
- The Gaussian Naive Bayes (GNB) model achieved high accuracy (0.81 in test set, 0.79 in validation set).
- Key predictors identified include multivessel CABG, history of heart failure, rs5219 (KCNJ11), and prolonged bypass duration.
- A web-based tool was developed for real-time POAF risk stratification.
Conclusions:
- The GNB classifier integrates pharmacogenomic and clinical predictors for POAF risk assessment post-CABG.
- The model serves as a valuable clinical decision-support tool.
- This approach enhances personalized perioperative care through rigorous validation and user-centered design.
Background:
Postoperative atrial fibrillation (POAF) is a frequent complication following coronary artery bypass grafting (CABG), significantly impacting patient prognosis and healthcare costs. This study aimed to develop an integrated predictive model for POAF risk stratification to optimize clinical management.
Methods:
We retrospectively analyzed 2,528 patients undergoing 21-gene pharmacogenetic testing for cardiovascular therapy. After stringent data curation, 576 CABG patients were enrolled and randomly allocated into training and test sets. Eight machine learning algorithms were trained using clinical variables and genetic variants. An independent validation set was performed on 61 patients from a subsequent 1,075-patient cohort of 21-gene pharmacogenetic testing.
Results:
Eight machine learning algorithms were trained, tested, and validated, with the Gaussian Naive Bayes (GNB) model demonstrating robust performance (Accuracy: 0.81 in test set and 0.79 in independent validation set). SHapley Additive exPlanations analysis identified four key predictors: multivessel CABG (CABGVx ≥ 3), history of heart failure (HFHx), rs5219 (KCNJ11), and prolonged bypass duration (CABGTime). To facilitate clinical translation, we developed an accessible web-based tool (https://www.xingyeyard.site/cabg/) for real-time POAF risk stratification.
Conclusion:
This GNB-based classifier synergistically integrates Pharmacogenomic and clinical predictors to predict POAF risk following CABG. The combination of rigorous validation and user-centered design positions this model as a valuable clinical decision-support tool for optimizing personalized perioperative care.
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