Electro-Mechanical Risk Score for Coronary Artery Disease Risk Estimation Using Seismocardiography

Abstract Background Coronary artery disease (CAD) remains a leading cause of morbidity. Existing clinical likelihood models often lack specificity, contributing to unnecessary diagnostic testing. Objectives To develop, train, and validate the Electro-Mechanical Risk (EMR) Score. This machine learning model uses cardiac mechanical information from resting seismocardiography (SCG) recordings and patient-level clinical risk factors to estimate obstructive CAD likelihood. Methods This multicentre clinical study included 2,110 adults. Resting SCG was recorded using a sternum accelerometer. Obstructive CAD was defined as 50% or greater stenosis on coronary computed tomography angiography or invasive coronary angiography. A one-dimensional convolutional neural network was trained to compute the EMR Score. Performance was evaluated using repeated cross-validation and external center validation and compared with the 2024 ESC Risk Factor–weighted Clinical Likelihood (RF-CL) model. Results Among 2,110 participants (mean [SD] age, 57.8 [10.3] years; 801 women [38%]), 760 had obstructive CAD. In symptomatic individuals, the EMR Score achieved an AUC of 0.88, outperforming RF-CL (AUC, 0.85; P = .023), with higher specificity (53% vs 35%) and comparable sensitivity (94% vs 97%). In external center validation, the EMR Score achieved an AUC of 0.91, sensitivity of 97.8%, specificity of 52.0%, PPV of 78.0%, and NPV of 93.0%. In asymptomatic participants, the AUC was 0.89. The EMR Score classified 23% of the cohort as very low likelihood, with 2% CAD prevalence. Conclusions The EMR Score noninvasively estimated obstructive CAD likelihood, with external-center validation supporting generalizability beyond internal cross-validation.

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