Implantable Heart Failure Risk Scoring With Threshold Lookup Tables
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting heart failure hospitalization risk are cumbersome, require complex models, and lack the ability to provide granular risk assessments, often burdening healthcare providers and failing to accurately estimate the likelihood of hospitalization.
Innovation Solution
A medical device system that collects and analyzes data from implantable devices to count data observations crossing metric thresholds, using a lookup table to estimate heart failure hospitalization risk with increased granularity and weighting recent data more heavily, allowing for more accurate and simplified risk prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use complex models and probability distribution functions for risk stratification, then measurement precision may be improved, but device complexity increases significantly
Solution Approach 1:
The patent replaces complex, resource-intensive probability distribution function models with a simpler lookup table approach. The lookup table stores pre-computed risk scores based on diagnostic metric observations, allowing the system to achieve accurate risk stratification without requiring computationally expensive modeling operations. This simplifies the device while maintaining measurement precision.
Solution Approach 2:
The system pre-computes and stores risk scores in a lookup table based on historical data and diagnostic metrics. During actual risk assessment, the system simply retrieves pre-calculated scores from the lookup table rather than performing complex real-time calculations. This preliminary preparation reduces computational complexity while maintaining precision.
2Reliability
If conventional methods use probability distribution functions and reference group comparisons, then reliability of risk prediction may be improved, but ease of operation deteriorates due to cumbersome procedures
Solution Approach 1:
The system automatically performs risk stratification by retrieving scores from the lookup table based on patient diagnostic metrics, without requiring healthcare providers to manually compare data against reference groups or perform complex statistical analyses. The device serves itself by automatically translating diagnostic observations into risk assessments, reducing provider burden while maintaining reliability.
Solution Approach 2:
The lookup table contains pre-computed risk scores that represent the results of complex probability distribution analyses. Instead of requiring providers to perform these complex calculations, the system copies the pre-determined risk scores from the lookup table based on matching diagnostic patterns, simplifying the operation while preserving the reliability of the underlying statistical models.
3Ease of operation
If conventional methods provide broad risk categories, then ease of operation is maintained, but measurement precision of risk assessment deteriorates
Solution Approach 1:
The lookup table stores risk scores across multiple granular levels, allowing the system to provide both broad categorical assessments and detailed granular risk values as needed. The system can adjust the level of detail in risk reporting based on clinical needs, maintaining ease of operation while enabling precise measurement when required.
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.