Heart Failure Risk Stratification via Patient Data Comparison
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Solution Overview
Problem
Current implantable medical devices lack effective methods for between-patient comparisons to predict heart failure decompensation, which is crucial for personalized patient management and risk stratification.
Innovation Solution
A system and method that involve receiving patient data, determining a reference group based on similar patients, generating a model using probability functions, and comparing the patient's data to this model to derive an index for risk assessment, allowing for stratification of patients into discrete risk levels for heart failure decompensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If implantable medical devices collect and store patient physiological data, then the data can be used for monitoring and analysis, but the devices lack the capability to perform between-patient comparisons for risk stratification
Solution Approach 1:
The patent introduces an external processing system (programmer or centralized server) as an intermediary between the implantable device and the risk stratification analysis. The implantable device collects and transmits physiological data, while the external system performs the complex between-patient comparisons and generates risk stratification. This separates the data collection function (simple) from the analysis function (complex), resolving the contradiction by placing the complex processing outside the implantable device.
2Measurement precision
If patient data is compared to reference groups for risk stratification, then prediction accuracy improves, but the complexity of determining and maintaining reference groups increases
Solution Approach 1:
The patent creates a universal reference group database that can serve multiple patients and multiple comparison scenarios. Instead of creating custom reference groups for each patient, a single centralized database stores physiological data from many patients, which can be queried and compared against any individual patient. This multi-functional database resolves the contradiction by providing accurate comparisons without the complexity of individualized reference group creation and maintenance.
3Reliability
If comprehensive patient criteria are used to select reference groups (age, gender, LVEF, medications, etc.), then the relevance of comparisons improves, but the difficulty of matching and updating reference groups increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors patient data and automatically updates reference group assignments. When a patient's physiological parameters or clinical status change, the system detects these changes and reassigns the patient to appropriate reference groups, ensuring ongoing relevance. This automated feedback loop resolves the contradiction by maintaining high relevance without manual intervention complexity.
Data Source
AI summary
This document discusses, among other things, systems and methods for using between-patient comparisons for risk stratification of future heart failure decompensation. A method comprises receiving patient data of a current patient, the patient data collected by a patient monitoring device; determining a reference group related to the patient; determining a reference group dataset selected from the reference group, wherein the dataset includes patient data that is of a similar type received from the patient monitoring device; generating a model of the reference group dataset; and automatically comparing the received physiological data to a model to derive an index for the patient.