Doppler Echocardiographic Phenotypes in Suspected 'Severe' Aortic Stenosis: Matrix-Based Approach to Diagnosis and Management

J Am Soc Echocardiogr. 2023 Oct 8:S0894-7317(23)00528-X. doi: 10.1016/j.echo.2023.09.010. Online ahead of print.

Abstract

Background: Among patients with suspected severe aortic stenosis (AS), Doppler echocardiographic (DE) data are often discordant, and further analysis is required for accurate diagnosis and optimal management. In this study, an automated matrix-based approach was applied to an echocardiographic database of patients with AS that identified 5 discrete echocardiographic data patterns, 1 concordant and 4 discordant, each reflecting a particular pathophysiology/measurement error that guides further workup and management.

Methods: A primary/discovery cohort of consecutive echocardiographic studies with at least 1 DE parameter of severe AS and analogous data from an independent secondary/validation cohort were retrospectively analyzed. Parameter thresholds for inclusion were aortic valve area (AVA) <1.0 cm2, transaortic mean gradient (MG) ≥ 40 mmHg, and/or transaortic peak velocity (PV) ≥ 4.0 m/sec. Doppler velocity index (DVI) was also determined. Logic provided by an in-line SQL query embedded within the database was used to assign each patient to 1 of 5 discrete matrix patterns, each reflecting 1 or more specific pathophysiologies. Feasibility of automated pattern-driven triage of discordant cases was also evaluated.

Results: In both cohorts, data from each patient fitted only 1 data pattern. Of the 4,643 primary cohort patients, 39% had concordant parameters for severe AS and DVI <0.30 (pattern 1); 35% had AVA < 1.0 cm2, MG < 40 mm Hg, PV < 4 m/sec, DVI < 0.30 (pattern 2); 9% had MG ≥ 40 mmHg and/or PV ≥ 4 m/sec, DVI > 0.30 (pattern 3); 10% had AVA < 1.0 cm2, MG < 40 mmHg, PV < 4 m/sec, DVI >0.30 (pattern 4); and 7% had MG > 40 mmHg and/or PV ≥ 4 m/sec, AVA > 1.0 cm2, DVI < 0.30 (pattern 5). Findings were validated among the 387 secondary cohort patients in whom pattern distribution was remarkably similar.

Conclusions: Matrix-based pattern recognition permits automated in-line identification of specific pathophysiology and/or measurement error among patients with suspected severe AS and discordant DE data.

Keywords: Pattern recognition; Severe aortic stenosis.