Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization
Amir Hossein Behnoush1,2,3,4, Amirmohammad Khalaji1,2,3,4, Malihe Rezaee1,2,4,5
1Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Insights
Machine learning models accurately predict 1-year mortality in hypertensive patients after coronary artery bypass graft (CABG) surgery. Logistic regression demonstrated the highest predictive accuracy, aiding risk assessment for high-risk subgroups.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Hypertensive patients face elevated mortality risks post-coronary artery bypass graft (CABG).
- Machine learning (ML) shows potential in improving medical predictions, including preventive cardiology.
- Accurate mortality prediction is crucial for managing high-risk patient groups.
Purpose of the Study:
- To develop and assess five machine learning models for predicting 1-year mortality in hypertensive patients undergoing CABG.
- To identify key predictors of mortality in this patient population.
- To compare the performance of different ML algorithms for this specific clinical application.
Main Methods:
- Utilized Tehran Heart Center's CABG registry data, including baseline and peri-procedural characteristics.
- Employed random forest (RF) for feature selection, identifying 11 significant predictors.
- Developed and evaluated five ML models: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB), using AUC, sensitivity, and specificity.
Main Results:
- The study included 8,493 hypertensive patients, with 303 deaths within the first year.
- Total ventilation hours and ejection fraction were identified as leading predictors of mortality.
- Logistic regression (LR) achieved the highest predictive performance with an AUC of 0.82, outperforming other models.
Conclusions:
- All developed ML models demonstrated excellent performance in predicting 1-year mortality for hypertensive CABG patients.
- Logistic regression (LR) emerged as the superior model based on AUC.
- These ML tools can assist clinicians in assessing mortality risk, particularly in high-risk subgroups like hypertensive patients.
Background:
Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG.
Hyothesis:
ML algorithms can significantly improve mortality prediction after CABG.
Methods:
Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models.
Results:
Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients.
Conclusions:
All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones).
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