Key result
An ECG-derived deep neural network model successfully predicted echocardiography-derived patient phenogroups, demonstrating an increased probability for major adverse cardiac events in high-risk versus low-risk patients (21% vs 3%, HR 8.17).
Why the study?
Systolic and diastolic dysfunction frequently coexist, necessitating an integrated assessment for optimal risk-stratification rather than isolated ECG-derived predictions of systolic or diastolic indices.
Does an ECG-derived machine learning model accurately predict echo-derived high-risk phenogroups for MACE in a multicenter cohort?
Cohort (n=1,245)
Yes
Does an ECG-derived machine learning model accurately predict echo-derived high-risk phenogroups for MACE in a multicenter cohort?
An ECG-based deep learning model can accurately emulate echocardiography-derived phenogroups to identify patients at high risk for major adverse cardiac events.
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.
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Patel et al. (2022) conducted a cohort in Cardiac risk-stratification (n=1,245). ECG-derived deep neural network model (High-risk phenogroup) vs. ECG-derived deep neural network model (Low-risk phenogroup) was evaluated on Major adverse cardiac events (MACE) (HR 8.17, 95% CI 3.97-16.82, p=<0.0001). An ECG-derived deep neural network model successfully predicted echocardiography-derived patient phenogroups, demonstrating an increased probability for major adverse cardiac events in high-risk versus low-risk patients (21% vs 3%, HR 8.17).
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