Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization
Cheng Cai1,2, Ahmad P Tafti3, Che Ngufor4
1Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Insights
A new machine learning model accurately predicts which heart failure patients will respond to cardiac resynchronization therapy (CRT), improving treatment selection and patient care.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Cardiac resynchronization therapy (CRT) is a treatment for heart failure, but approximately 30% of patients do not respond.
- Identifying CRT responders is crucial for effective treatment and resource allocation.
Purpose of the Study:
- To develop and evaluate a novel machine learning (ML) system for predicting patient response to CRT.
- To compare the predictive performance of the developed system against traditional ML methods and a convolutional neural network (CNN).
Main Methods:
- Retrospective analysis of 1664 patient electronic health records from 2002-2017.
- Development of an ensemble of ensemble (EoE) ML system with supervised and unsupervised layers.
- Comparison of EoE model performance against traditional ML and CNN models trained on ECG waveforms.
Main Results:
- The EoE model demonstrated improved performance with increased feature utilization.
- The EoE model achieved an area under the receiver operating characteristic curve of 0.76 and an F1-score of 0.73.
- A CNN model directly applied to raw ECG waveforms showed less promising results.
Conclusions:
- The proposed CRT risk calculator significantly outperforms clinical guidelines and traditional ML methods in identifying likely CRT responders.
- This tool can enhance heart failure patient care management by identifying high-risk individuals for CRT.
Introduction:
The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.
Methods:
We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response.
Results:
We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results.
Conclusion:
The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.
Related Concept Videos
01:25Correlation between ECG and Cardiac Cycle
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
01:19Electrophysiology of Normal Cardiac Rhythm
01:29Cardiomyopathy V: Interprofessional Care
01:30Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...