JACC. Advances · 2026
Wearable SENsor to Diagnose and Assess SEverity of Aortic Stenosis (SENSE-AS): A Proof-of-Concept Study.
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Srinivasan Aniruddh, El Harake Lamis, Torres Fernandez Edgar D, Saleeb John, Trueb Jacob, Flaherty James D, Sweis Ranya, Shah Sanjiv, Thomas James D, Narang Akhil, Rogers John A, Du Yayun, Cremer Paul C
Аннотация
BACKGROUND: Aortic stenosis (AS) is a common disease with delays in diagnosis and treatment. Wearable sensors may provide a scalable, noninvasive method to diagnose, assess severity, and monitor AS.
OBJECTIVES: The primary objective was to quantify the correlation between Doppler-derived aortic valve (AV) acceleration time (AT) and sensor-derived AT; the secondary objective was to train machine learning models using sensor data to predict maximal transvalvular velocity (Vmax).
METHODS: This prospective, single-center case-control study enrolled 40 subjects with severe (n = 10), moderate (n = 10), and mild (n = 10) AS according to echocardiography guidelines, along with controls without AS (n = 10) who were age- and sex-matched to the severe AS cohort. At the time of echocardiography, patients wore a sensor simultaneously recording electrocardiogram, seismocardiogram, and phonocardiogram signals.
RESULTS: The mean age across groups was 77.8 years, and 55% were female (n = 22). Sensor-derived AT showed a strong correlation with Doppler AT (r = 0.85; 95% CI: 0.73-0.92; P < 0.001). Groupwise comparisons demonstrated progressively longer AT from controls through increasing AS severities (P < 0.01). Predictions on unseen patient data from a machine learning model trained on a limited subset of AS patient recordings (n = 32) correlated with AV Vmax (r = 0.76; 95% CI: 0.68-0.83; P < 0.001).
CONCLUSIONS: A chest-worn multimodal wearable sensor accurately measures AV AT and predicts AV Vmax, supporting the feasibility of accessible, lower-cost tools for early detection and longitudinal monitoring of AS.
OBJECTIVES: The primary objective was to quantify the correlation between Doppler-derived aortic valve (AV) acceleration time (AT) and sensor-derived AT; the secondary objective was to train machine learning models using sensor data to predict maximal transvalvular velocity (Vmax).
METHODS: This prospective, single-center case-control study enrolled 40 subjects with severe (n = 10), moderate (n = 10), and mild (n = 10) AS according to echocardiography guidelines, along with controls without AS (n = 10) who were age- and sex-matched to the severe AS cohort. At the time of echocardiography, patients wore a sensor simultaneously recording electrocardiogram, seismocardiogram, and phonocardiogram signals.
RESULTS: The mean age across groups was 77.8 years, and 55% were female (n = 22). Sensor-derived AT showed a strong correlation with Doppler AT (r = 0.85; 95% CI: 0.73-0.92; P < 0.001). Groupwise comparisons demonstrated progressively longer AT from controls through increasing AS severities (P < 0.01). Predictions on unseen patient data from a machine learning model trained on a limited subset of AS patient recordings (n = 32) correlated with AV Vmax (r = 0.76; 95% CI: 0.68-0.83; P < 0.001).
CONCLUSIONS: A chest-worn multimodal wearable sensor accurately measures AV AT and predicts AV Vmax, supporting the feasibility of accessible, lower-cost tools for early detection and longitudinal monitoring of AS.