Finger Cuff Algorithm Enables Noninvasive Screening for Aortic Stenosis

Symptoms such as fatigue, dyspnea, and dizziness often mimic normal aging, delaying recognition. Missed or late diagnoses are especially concerning in populations with documented disparities in detection and outcomes. To help address this challenge, researchers have developed and evaluated a noninvasive algorithm that analyzes finger cuff signals to flag moderate-to-severe disease.

The approach pairs the ASI algorithm with the Acumen IQ finger cuff from Edwards Lifesciences to screen for clinically significant aortic stenosis (AS). Results were presented at the 2026 Scientific Sessions of the Society for Cardiovascular Angiography & Interventions (SCAI) and the Canadian Association of Interventional Cardiology (CAIC-ACCI) Summit in Montreal. The investigation drew clinical commentary from Henry Ford Health, reflecting interest from health systems that manage large cardiovascular populations. The aim was to offer a bedside-friendly screening method that could accelerate referral for definitive imaging and treatment.

The Recognition & Evaluation of Aortic Stenosis to Create Health (REACH) trial was a prospective, non-randomized, unblinded study conducted at three U.S. sites. Participants were assigned to two cohorts—those with moderate-to-severe AS and those without—using echocardiography for confirmation. Edwards Lifesciences' Acumen IQ cuff, an air-filled device placed around the finger, continuously recorded arterial pulse and pressure waveforms. Clinicians then applied the ASI algorithm to these data to identify moderate-to-severe AS and assessed sensitivity and specificity.

Among 346 patients, 47.1% were male and 26.9% were African American. The algorithm detected moderate-to-severe AS with 90.5% sensitivity in the overall cohort, with a 95% confidence interval of 84.6 to 96.4. Sensitivity reached 100% in African American patients. Specificity was 70.9% overall (95% confidence interval 65.0 to 76.8) and 73.0% in African American patients (95% confidence interval 63.2 to 82.8). These findings support the algorithm’s suitability for screening.

The work addresses evidence that older Black Americans experience lower rates of AS diagnosis yet higher mortality risk, underscoring a need for accessible monitoring in this group. Future studies are planned to determine how the technology can guide referrals to definitive therapy in communities facing inequities.

“The ASI algorithm, paired with the Acumen cuff, performed consistently well across age, gender, and racial groups, showing no signs of bias and demonstrating strong performance in screening for moderate-to-severe aortic stenosis,” said Pedro Engel Gonzalez, MD, cardiologist at Henry Ford Health in Detroit, Michigan. “Our findings give us hope for communities that are more likely to experience limitations to care. Something as simple as a finger cuff and an algorithm can help improve early diagnoses and get patients the care that they need."

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