Patient-Specific Cardiovascular Model for Hemodynamic Management
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Solution Overview
Problem
Managing the complex hemodynamic state of heart failure patients, particularly those with ventricular assist devices (VADs) and additional conditions, requires sophisticated monitoring and data interpretation to optimize therapy and patient quality of life, but existing systems lack a unified remote monitoring infrastructure and effective data processing algorithms.
Innovation Solution
A cloud-based remote monitoring and simulation system that builds and updates patient-specific models using machine learning and optimization techniques, integrating remote monitoring data and clinical data to perform simulations and output recommendations for treatment optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a unified remote monitoring infrastructure and data processing algorithms are implemented, then hemodynamic management accuracy and treatment optimization improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces a centralized remote monitoring server as an intermediary that consolidates data processing functions. This server receives data from multiple VAD devices, performs centralized algorithmic processing, and generates treatment recommendations, thereby improving measurement precision while managing system complexity through architectural separation.
Solution Approach 2:
The patent creates a digital replica or model of the patient's hemodynamic system through sophisticated data processing algorithms. This virtual model allows for accurate simulation and prediction of hemodynamic states without requiring direct complex intervention in the physical system, thus improving accuracy while controlling complexity.
2Ease of operation
If sophisticated sensing abilities and closed loop control algorithms are added to VADs, then patient quality of life and therapy optimization improve, but device complexity and physician training requirements increase
Solution Approach 1:
The patent implements closed-loop control algorithms that enable the VAD system to automatically adjust therapy parameters based on real-time hemodynamic data. The system self-regulates without requiring constant physician intervention, thereby improving ease of operation and patient quality of life while the automated nature helps manage complexity by reducing manual configuration needs.
Solution Approach 2:
The patent incorporates sophisticated sensing abilities that continuously monitor hemodynamic parameters and provide feedback to the control system. This closed-loop feedback mechanism allows the VAD to automatically optimize therapy based on real-time patient status, improving quality of life while the automated feedback processing helps manage device complexity through intelligent algorithms.
3Adaptability or versatility
If multiple devices and diagnostic tools are integrated into the patient management system, then diagnostic capability and treatment optimization improve, but interface complexity and data interpretation difficulty increase
Solution Approach 1:
The patent merges data from multiple VAD devices, pacemakers, and diagnostic tools into a unified remote monitoring platform. By consolidating these diverse data sources and interfaces into a single centralized system, the patent improves diagnostic capability and versatility while reducing interface complexity through unified access and integrated data presentation.
Data Source
AI summary
Systems and methods for performing personalized cardiovascular analyses are provided. A method includes building, using a modeling and simulation computing device, a patient-specific model, storing, using the modeling and simulation computing device, the patient-specific model in a database, receiving, at the modeling and simulation computing device, remote monitoring data from at least one remote monitoring data source, and receiving, at the modeling and simulation computing device, clinical data from at least one clinical data source. The method further includes updating, using the modeling and simulation computing device, the patient-specific model using the remote monitoring data and the clinical data, performing, using the modeling and simulation computing device, at least one simulation on the updated patient-specific model, and outputting, from the modeling and simulation computing device, at least one output based on the at least one simulation.