Implantable Device Data Analysis for Heart Failure Hospitalization Risk
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Solution Overview
Problem
Existing methods for predicting heart failure hospitalization risk are cumbersome, require complex modeling, and lack granular risk assessment, often burdening healthcare providers and providing only broad risk categories.
Innovation Solution
A method using implantable medical devices to collect and analyze cardiac and thoracic data, calculating HFH risk by counting data observations within evaluation periods and creating a lookup table for more precise risk estimation, allowing for increased gradations of risk without undue burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex modeling methods (probability distribution functions, reference group comparisons) are used to predict HFH risk, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent segments the continuous risk prediction problem into discrete risk levels (e.g., low, medium, high risk categories). Instead of using complex continuous probability models, the system divides the prediction space into manageable segments that can be assessed using simpler rules and thresholds, reducing computational complexity while maintaining clinical utility.
Solution Approach 2:
The patent replaces complex, resource-intensive probability distribution models with simpler, more efficient algorithms that require fewer computational resources. The system uses straightforward risk scoring methods that can be quickly calculated and updated, eliminating the need for maintaining complex reference group datasets and probability models.
2Measurement precision
If complex probability distribution modeling is used, then measurement precision improves, but ease of operation worsens due to burden on healthcare providers
Solution Approach 1:
The system enables automated risk assessment that requires minimal manual intervention from healthcare providers. The device automatically collects patient data, applies risk algorithms, and generates risk level classifications without requiring providers to manually configure complex models or interpret probability distributions, significantly reducing operational burden.
Solution Approach 2:
The patent transforms the output of complex probability models into simplified risk level parameters (categorical classifications). By changing the parameter representation from continuous probabilities to discrete risk levels, the system maintains predictive accuracy while making the results more interpretable and easier for providers to act upon.
3Ease of operation
If broad risk categories are used, then ease of operation improves, but measurement precision deteriorates due to lack of granular risk assessment
Solution Approach 1:
The patent applies different levels of granularity to different aspects of risk assessment. Within each risk level category, the system provides detailed breakdowns of contributing factors and sub-metrics, allowing providers to access granular information when needed while maintaining simple overall categorization for quick assessment. This local differentiation of quality meets both simplicity and precision requirements.
Data Source
AI summary
A method of operation of a medical device system for determining prospective heart failure hospitalization risk. The method includes measuring one or more data observations via one or more electrodes of an implanted medical device disposed in a patient's body. The data observations are stored into memory of the implantable medical device of a patient. The data observations are transmitted to an external device. The processor of the external device parses the data observations into one or more evaluation periods. Using the number of observations in one or more evaluation periods, a look up table, stored into memory of the external device, is accessed. The look up table associates prospective heart failure hospitalization risk with the data observations noted in the evaluation period. One or more embodiments involve a weighted prospective heart failure hospitalization risk for the set of evaluation periods. The prospective heart failure hospitalization is then displayed on the graphical user interface.