A groundbreaking algorithm identifies moderate-to-severe aortic stenosis (AS) with 90.5% sensitivity across patients and 100% accuracy in African American individuals. This tool offers hope for earlier detection in underserved groups facing higher mortality risks.
Understanding Aortic Stenosis
Aortic stenosis is a prevalent heart valve condition that progresses over time. Untreated severe cases lead to death in half of symptomatic patients within two years. Symptoms such as fatigue, shortness of breath, and dizziness often mimic normal aging, delaying diagnosis. Older Black Americans experience lower diagnosis rates despite elevated mortality, highlighting the urgency for equitable screening.
REACH Trial Overview
The Recognition & Evaluation of Aortic Stenosis to Create Health (REACH) trial, a prospective, non-randomized, unblinded study at three U.S. sites, evaluated the algorithm. Participants formed two cohorts: one with moderate-to-severe AS and one without, verified by echocardiography.
Clinicians used the Acumen IQ cuff from Edwards Lifesciences, an air-filled device on the finger that tracks arterial pulse and pressure continuously. The ASI algorithm analyzed cuff data to screen for AS, optimizing sensitivity (ability to detect true cases) and specificity (ability to rule out healthy individuals).
Patient Demographics
The cohort included 346 patients, with 47.1% (163) male and 26.9% (93) African American.
Key Performance Results
The algorithm achieved 90.5% sensitivity for moderate-to-severe AS overall (95% CI: 84.6-96.4) and 100% in African American patients. Specificity reached 70.9% overall (CI: 65-76.8) and 73% in African American patients (CI: 63.2-82.8).
Results demonstrate consistent performance across age, gender, and race, with no bias detected. The combination of ASI algorithm and Acumen cuff excels in screening for moderate-to-severe AS.
Expert Insights and Next Steps
“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,” stated Pedro Engel Gonzalez, MD, cardiologist at Henry Ford Health in Detroit, Michigan.
Future research will explore integrating this technology into referral processes to address care inequities and enhance treatment access.
