Related Experiment Video
Updated: Jul 14, 2025
Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
Published on: February 14, 2017
Doppler Echocardiographic Phenotypes in Suspected 'Severe' Aortic Stenosis: Matrix-Based Approach to Diagnosis and
Richard H Marcus1, Russell Hamilton1, Justin Ugwu1
1Division of Cardiovascular Medicine, Iowa Heart Center, Des Moines, Iowa.
Insights
This study introduces an automated matrix-based approach to classify patients with suspected severe aortic stenosis (AS) based on Doppler echocardiographic (DE) data patterns. This method accurately identifies specific pathophysiologies and measurement errors, aiding in diagnosis and patient management.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Severe aortic stenosis (AS) diagnosis can be challenging due to discordant Doppler echocardiographic (DE) data.
- Accurate diagnosis is crucial for optimal patient management.
Purpose of the Study:
- To develop and validate an automated matrix-based approach for classifying patients with suspected severe AS.
- To identify distinct echocardiographic data patterns reflecting specific pathophysiologies or measurement errors.
Main Methods:
- Retrospective analysis of echocardiographic studies from primary and secondary cohorts.
- Inclusion criteria: aortic valve area (AVA) <1.0 cm², mean gradient (MG) ≥40 mmHg, and/or peak velocity (PV) ≥4.0 m/sec.
- Application of an automated matrix-based logic to assign patients into 5 discrete patterns based on DE parameters and Doppler velocity index (DVI).
Main Results:
- The automated approach successfully classified all patients into one of five distinct patterns in both cohorts.
- Pattern distribution was consistent across the primary (n=4,643) and secondary (n=387) cohorts.
- Identified patterns included concordant severe AS (39%) and various discordant patterns reflecting different pathophysiologies/errors.
Conclusions:
- Matrix-based pattern recognition enables automated, in-line identification of specific pathophysiology and/or measurement errors in patients with suspected severe AS.
- This approach facilitates more accurate diagnosis and guides appropriate further workup and management strategies.
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.
More Related Videos
06:51Author Spotlight: Development of a Minimally Invasive Large-Animal Model for Reliable and Reproducible Cardiovascular Research
Published on: October 20, 2023
09:05Assessment of Cardiac Morphological and Functional Changes in Mouse Model of Transverse Aortic Constriction by Echocardiographic Imaging
Published on: June 21, 2016
Related Concept Videos
01:23Mitral Stenosis II: Clinical features and Diagnostic Tests
01:29Cardiomyopathy III: Hypertrophic Cardiomyopathy
01:22Aortic Regurgitation II: Clinical Features and Diagnostic Tests
01:20Imaging Studies for Cardiovascular System II:Types of Echocardiography
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...