surface electrocardiography, echocardiography, diastolic dysfunction, machine learning, topological data analysis
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.
Patel HB, Yanamala N, Patel B, Raina S, Farjo PD, Sunkara S, Tokodi M, Kagiyama N, Casaclang-Verzosa G, Sengupta PP. Electrocardiogram-based machine learning emulator model for predicting novel echocardiography-derived phenogroups for cardiac risk-stratification: a prospective multicenter cohort study. J Patient Cent Res Rev. 2022;9:98-107. doi: 10.17294/2330-0698.1893
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July 24th, 2021
November 30th, 2021