CFD Thromboembolic Risk Estimation for Myocardial Infarction
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Solution Overview
Problem
Current methods for risk stratification of thromboembolic events in patients with myocardial infarction are inadequate, leading to unnecessary triple therapy that increases bleeding risk and fails to effectively prevent left ventricular thrombus formation, as they rely primarily on global ventricular function and image-based assessments.
Innovation Solution
A method involving high-resolution imaging of the heart, construction of a patient-specific computational fluid dynamics model, calculation of thrombogenic metrics, and generation of a thrombogenic risk assessment using metrics such as particle residence time, wall shear stress, and platelet concentration to identify patients at high risk of thromboembolic events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current risk stratification methods based on global ventricular function and image-based assessment are used, then the assessment process is simple, but the precision of thromboembolic risk identification is insufficient leading to inadequate risk stratification
Solution Approach 1:
The patent segments the global ventricular function assessment into localized regional wall motion analysis. By dividing the left ventricle into multiple segments and analyzing wall motion in each segment independently, the method identifies specific areas of akinesis or dyskinesis that are more precisely associated with thrombus formation risk, thereby improving measurement precision without requiring overly complex global modeling
Solution Approach 2:
The patent transitions from two-dimensional image-based assessment to three-dimensional computational fluid dynamics modeling. By creating a 3D patient-specific model of the left ventricle and simulating blood flow dynamics, the method captures hemodynamic parameters (such as flow stagnation and wall shear stress) that cannot be obtained from conventional 2D imaging, significantly improving risk identification precision
2Reliability
If triple therapy is applied to all high-risk patients identified by current methods, then thromboembolic prevention coverage is increased, but bleeding risk increases significantly to 22.6% in the first 30 days
Solution Approach 1:
The patent applies local quality by identifying specific regional wall motion abnormalities and localized flow stagnation patterns rather than relying on global ventricular function alone. By focusing on localized hemodynamic parameters in specific ventricular segments, the method more accurately identifies patients who truly benefit from triple therapy, reducing unnecessary treatment and associated bleeding risks
Solution Approach 2:
The patent changes the parameters used for risk stratification from conventional clinical parameters (global LVEF, standard imaging findings) to advanced hemodynamic parameters derived from CFD modeling (flow velocity, residence time, wall shear stress). This parameter transformation enables more precise identification of high-risk patients, allowing for targeted triple therapy that maintains effectiveness while reducing overall bleeding risk by excluding low-risk patients
3Ease of operation
If current image-based assessment methods are used, then the assessment is non-invasive and easy to perform, but the ability to detect flow stasis and predict thrombus formation is insufficient
Solution Approach 1:
The patent introduces computational fluid dynamics modeling as an intermediary between conventional imaging and thrombus risk assessment. The CFD model acts as a mediator that translates standard imaging data into detailed hemodynamic parameters, bridging the gap between easily obtainable images and the precise flow stasis detection needed for accurate thrombus prediction
Solution Approach 2:
The patent creates a virtual copy of the patient's left ventricle through 3D computational modeling based on imaging data. This digital twin allows for non-invasive simulation of blood flow patterns and hemodynamic analysis without requiring additional invasive procedures, maintaining ease of operation while dramatically improving flow stasis detection precision
Data Source
AI summary
The present invention provides a method for determining thromboembolic risk in a patient. The method includes processing functional images of a patient's heart in order to create a computational fluid dynamic (CFD) modeling of the patient's heart. Once a CFD model is obtained, various metrics can be determined to estimate the patient's risk of left ventricular thrombosis. This method is particularly suited for determining thromboembolic risk in patients having suffered a myocardial infarction. However, the method can also be applied to a broader population at risk of cardioembolic and cryptogenic stroke.