Aortic Aneurysm Risk Index Using Computational Modeling
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Solution Overview
Problem
Current methods for managing ascending thoracic aortic aneurysms (ATAA) are inadequate as they rely on maximum aortic diameter measurements, which have low prognostic value and are not personalized, leading to aggressive surgical interventions that may not prevent 40% of dissections and pose risks to older patients with increasing healthcare costs.
Innovation Solution
A method and system that integrate clinical, demographic, biochemical, and morphological data, including non-coding RNA biomarkers, to calculate a personalized risk index for aortic rupture or dissection using computational modeling and fluid-structure analysis, providing a more accurate and tailored approach to clinical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgical intervention is performed based on maximum aortic diameter threshold (5.0 cm), then the risk of rupture or dissection is reduced, but the surgical risks and healthcare costs increase for older patients
Solution Approach 1:
The patent changes the parameter basis for surgical decision-making from a single anatomical parameter (maximum aortic diameter) to a composite risk index incorporating multiple parameters including non-coding RNA biomarkers, clinical characteristics, and morphological features. This allows for personalized risk assessment that can identify patients who truly need surgery versus those who can be monitored conservatively.
Solution Approach 2:
The patent replaces the simple mechanical threshold-based decision system (5.0 cm diameter cutoff) with a computational modeling system that integrates biochemical, clinical, and morphological data. This substitution enables more nuanced risk stratification that accounts for individual patient variability rather than applying a uniform threshold.
2Measurement precision
If close monitoring of aortic diameter is performed every 6-12 months, then the progression of aneurysm can be detected, but complications may occur during the monitoring period
Solution Approach 1:
The patent performs preliminary risk assessment using the composite risk index before initiating monitoring or treatment. By identifying high-risk patients early through non-coding RNA biomarkers and computational modeling, the system can intensify monitoring or recommend earlier intervention for those most likely to develop complications, rather than applying uniform monitoring intervals to all patients.
Solution Approach 2:
The patent establishes a feedback mechanism where the composite risk index is continuously updated as new clinical, biochemical, and morphological data become available. This dynamic risk assessment allows monitoring intensity and intervention timing to be adjusted based on individual patient risk trajectories rather than fixed schedules.
3Ease of operation
If maximum aortic diameter is used as the sole criterion for surgical decision-making, then the process is simple and standardized, but the prognostic value is low and personalization is lost
Solution Approach 1:
The patent segments the risk assessment process into distinct components: non-coding RNA biomarker analysis, clinical characteristic evaluation, morphological feature assessment, and computational modeling. Each component contributes to the overall composite risk index, allowing for systematic integration of multiple data types while maintaining structured decision-making.
Solution Approach 2:
The patent creates a composite risk index that integrates diverse data types (biochemical biomarkers, clinical parameters, morphological measurements) analogous to composite materials combining different properties. This composite approach leverages the strengths of each data type to achieve more accurate and personalized prognostic assessment than any single parameter could provide.
Data Source
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AI summary
A method for calculating the risk of aortic rupture or dissection of an individual with an ascending thoracic aortic aneurysm, ATAA, comprising the steps of obtaining (10) a first data set (12) linked to the clinical and/or demographic characteristics of the individual, obtaining (20) a second data set (22) linked to the biochemical characteristics of a biological sample of the individual, obtaining (30) a third data set (32) linked to the morphological and functional characteristics of the aorta and processing said third data set (32) to obtain (40) a fourth data set (42) by computational modelling, integrating the first data set (12), the second data set (22), the third data set (32) and the fourth data set (42) in a predictive model to obtain a risk index (i) of aortic rupture or dissection, wherein the second data set (22) comprises expression values of at least one biomarker.