文章基本信息

标题：Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study
本地全文：下载
作者：Heenaben B. Patel ; Naveena Yanamala ; Brijesh Patel 等
期刊名称：Journal of Patient-Centered Research and Reviews
电子版ISSN：2330-0698
出版年度：2022
卷号：9
期号：2
页码：98-107
DOI：10.17294/2330-0698.1893
语种：English
出版社：Aurora Health Care
摘要：Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE). Methods: In this substudy of a prospective, multicenter study, patients from 3 institutions (n = 727) formed an internal cohort, and the fourth institution was reserved as an external test set (n = 518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset. Results: Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.80–0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P < 0.001), which was similar to the echo-trained model (21% vs 5%; P < 0.001), suggesting comparable utility. Conclusions: This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE.