Key result
An ensemble of ensemble machine learning model predicted cardiac resynchronization therapy response with an area under the receiver operating characteristic curve of 0.76 and an F1-score of 0.73.
Why the study?
About 30% of candidates fail to respond to CRT, and smart computational approaches can help identify hidden patterns to predict CRT responders.
Does an ensemble of ensemble machine learning system improve the prediction of CRT response in heart failure patients compared to traditional methods?
Observational (n=1,664)
Does an ensemble of ensemble machine learning system improve the prediction of CRT response in heart failure patients compared to traditional methods?
An ensemble machine learning approach can effectively predict which heart failure patients are likely to respond to cardiac resynchronization therapy, outperforming traditional methods and raw ECG CNN models.
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.
No takes yet. Share an insight, caveat, or question.
Cai et al. (2021) conducted an observational in Heart failure (n=1,664). Ensemble of ensemble (EoE) machine learning model vs. Traditional machine learning methods, CNN, and clinical guidelines was evaluated on Prediction of CRT response (AUC 0.76). An ensemble of ensemble machine learning model predicted cardiac resynchronization therapy response with an area under the receiver operating characteristic curve of 0.76 and an F1-score of 0.73.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: