ArticleJACC. Advances2026
Wearable SENsor to Diagnose and Assess SEverity of Aortic Stenosis (SENSE-AS): A Proof-of-Concept Study.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAortic 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.
objectivesThe 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).
methodsThis 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.
resultsThe 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).
conclusionsA 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.
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