Adaptive Heart Failure Detection Thresholds for Co-morbidities
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Solution Overview
Problem
Current implantable medical devices (IMDs) face challenges in accurately monitoring hemodynamic parameters and detecting worsening heart failure (HF) in patients with co-morbidities, as symptoms and measurement patterns differ significantly between individuals with and without co-morbidities, necessitating adaptive detection thresholds and sensor signal selection based on specific co-morbidities.
Innovation Solution
A system comprising a sensor signal processor, memory for co-morbidity information, sensor signal selection circuit, threshold adjustment circuit, and decision circuit that selects and adjusts sensor signals according to indicated co-morbidities to detect worsening HF, using various sensors such as activity, cardiac, and respiration sensors, and outputs indications to users or processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detection threshold is used for all patients, then the device complexity is reduced, but the measurement precision deteriorates for patients with co-morbidities
Solution Approach 1:
The detection threshold is made dynamic and adaptive rather than fixed. The system automatically adjusts the threshold based on the patient's specific co-morbidities, activity levels, and physiological characteristics. This allows the device to optimize detection accuracy for each individual patient while maintaining a relatively simple overall device architecture through automated adaptation.
Solution Approach 2:
The system changes the detection threshold parameter based on patient-specific factors such as co-morbidities (e.g., COPD, diabetes, renal disease), activity level, and physiological state. By modifying this key parameter adaptively, the device achieves high measurement precision across diverse patient populations without requiring complex manual configuration.
2Reliability
If multiple sensor signals are monitored for all patients, then the detection reliability is improved, but the device complexity increases
Solution Approach 1:
The system applies local quality by selecting and monitoring specific sensor signals based on the individual patient's co-morbidities and clinical needs. Rather than continuously monitoring all available sensors for all patients, the device tailors the monitoring strategy to each patient's specific condition, optimizing reliability while minimizing unnecessary complexity.
Solution Approach 2:
The device incorporates a universal sensor suite that can serve multiple functions and patient types. The same physical sensors can be used for different monitoring purposes depending on the patient's needs, allowing the system to achieve high reliability across diverse populations without requiring separate dedicated sensors for each condition.
3Measurement precision
If co-morbidity-specific monitoring is implemented, then the measurement precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically selecting appropriate sensor signals and adjusting detection thresholds based on the patient's co-morbidities and physiological state. The device autonomously adapts to each patient's specific needs without requiring manual configuration by operators, thereby maintaining high measurement precision while preserving ease of operation.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor patient responses and physiological parameters, automatically adjusting the monitoring strategy and detection thresholds in real-time. This closed-loop approach ensures optimal detection accuracy for each patient's specific condition while eliminating the need for manual intervention.
Data Source
AI summary
This document discusses, among other things, a system comprising a sensor signal processor configured to receive a plurality of electrical sensor signals produced by a plurality of sensors and at least one sensor signal produced by an implantable sensor, a memory that includes information indicating a co-morbidity of a subject, a sensor signal selection circuit that selects a sensor signal to monitor from among the plurality of sensor signals, according to an indicated co-morbidity, a threshold adjustment circuit that adjusts a detection threshold of the selected sensor signal according to the indicated co-morbidity, and a decision circuit that applies the adjusted detection threshold to the selected sensor signal to determine whether an event associated with worsening heart failure (HF) occurred in the subject and outputs an indication of whether the event associated with worsening HF occurred to a user or process.