In Journal of cardiovascular electrophysiology
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 1,664 patients who underwent CRT procedures from Jan 1, 2002 to Dec 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. This article is protected by copyright. All rights reserved.
Cai Cheng, Tafti Ahmad P, Ngufor Che, Zhang Pei, Ko Wei-Yin, Xiao Peilin, Dai Mingyan, Liu Hongfang, Noseworthy Peter, Chen Minglong, Friedman Paul A, Cha Yong-Mei
artificial intelligence, cardiac resynchronization therapy, heart failure, machine learning, prediction