Utilizing a wide spectrum of data — traditional and signal processed ECG, patient demographics, and comorbidities — successfully predicted echocardiographically defined patient subgroups at high risk of major adverse cardiovascular events. Results demonstrate the potential value of machine learning-driven algorithms for rapid decision-making in an office-based setting to evaluate and monitor the progress of the patient and justify appropriate downstream referral for additional tests like echocardiography or other interventions.
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.
