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Published on: July 20, 2022
Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery
Orlando Parise1,2, Gianmarco Parise1, Akshayaa Vaidyanathan3
1Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.
Insights
The Random Forest machine learning model effectively predicts new-onset postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG), identifying key clinical factors for improved patient outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Postoperative atrial fibrillation (POAF) is a common complication following coronary artery bypass grafting (CABG).
- Predicting POAF is crucial for optimizing patient care and reducing adverse events.
Purpose of the Study:
- To develop an effective machine learning (ML) model for predicting new-onset POAF after CABG.
- To identify the most significant clinical factors associated with POAF development.
Main Methods:
- Four ML algorithms (Multivariate Adaptive Regression Spline, Neural Network, Random Forest, Support Vector Machine) were compared using a dataset of 394 CABG patients.
- Model performance was evaluated using Receiver Operating Characteristic Area Under the Curve after hyperparameter tuning.
- A logistic regression model was included for comparative analysis.
Main Results:
- The Random Forest model demonstrated superior performance in predicting POAF.
- Key predictive features identified include age, preoperative creatinine, aortic cross-clamping time, body surface area, and Logistic Euro-Score.
Conclusions:
- Machine learning models require rigorous evaluation, including hyperparameter optimization, for reliable clinical prediction.
- Random Forest emerged as the best-performing model for predicting POAF in CABG patients.
Background:
This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.
Methods:
Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models.
Results:
Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score.
Conclusions:
The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.
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