Sequential Heart Failure Detection Using Composite Sensor Values
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Solution Overview
Problem
Current systems for detecting heart failure in patients face challenges in accurately identifying changes in heart failure status while minimizing false alarms, due to the subtle and erratic onset of symptoms, reliance on regular check-ups, and variability in patient factors such as age and pre-existing conditions, leading to undiagnosed conditions and unnecessary healthcare resource expenditure.
Innovation Solution
A method and system utilizing a sequential two-stage analysis process with composite values from multiple sensors, including respiration rate and trans-thoracic impedance sensors, to generate alerts for changes in heart failure status, where the first composite value screens for early indicators and the second composite value provides a more specific prediction, with adjustable ranges for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems use regular check-ups and single-threshold detection, then device complexity is reduced, but measurement precision and reliability of heart failure detection deteriorate
Solution Approach 1:
The detection system is divided into two sequential stages: a first classifier using a single sensor measurement with a first threshold, and a second classifier using a different sensor measurement with a second threshold. This segmentation allows the system to achieve high detection accuracy by analyzing multiple parameters sequentially, while keeping each individual classification step relatively simple.
Solution Approach 2:
The system changes detection parameters by using different sensor measurements (e.g., respiration rate, trans-thoracic impedance, activity level) at different stages. The first classifier uses one parameter set with specific thresholds, and the second classifier uses another parameter set with different thresholds, allowing flexible adaptation to various patient conditions and improving overall detection precision.
2Reliability
If current systems rely on single sensor measurements and fixed thresholds, then ease of operation is improved, but reliability of detection deteriorates due to false alarms and missed diagnoses
Solution Approach 1:
The system dynamically adapts thresholds based on patient-specific baseline measurements and historical data. Instead of using fixed universal thresholds, the system learns and adjusts thresholds for each patient, improving reliability by accounting for individual variability in physiological parameters while maintaining automated operation.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and patient responses are used to refine future detections. The sequential classification approach provides feedback at each stage, allowing the system to adjust its detection strategy and improve reliability over time while maintaining ease of operation through automated decision-making.
3Reliability
If current systems perform comprehensive monitoring of all patients, then detection coverage is improved, but healthcare resource expenditure increases due to false alarms
Solution Approach 1:
The sequential two-stage classification system segments the detection process to efficiently allocate resources. The first classifier screens all patients with a simple single-threshold test, and only patients who trigger this first stage proceed to the more resource-intensive second classifier. This segmentation ensures comprehensive monitoring coverage while minimizing false alarms and reducing unnecessary healthcare resource expenditure.
Solution Approach 2:
The system applies partial monitoring intensity to different patient groups based on risk stratification. Low-risk patients undergo simpler first-stage screening, while only patients showing concerning signs proceed to the more comprehensive second-stage analysis. This approach maintains reliable detection coverage for high-risk cases while reducing resource expenditure on low-risk patients who are unlikely to have heart failure.
Data Source
AI summary
A system and method for automatically analyzing heart failure in a patient, including collecting physiological data from a patient using at least a first sensor and a second sensor to collect two or more sensor measurements, and calculating a first composite value based on at least a first sensor measurement wherein the first composite value is an indication of a likelihood that the patient's heart failure status has changed. If the first composite value is outside of a first specified range, then a second composite value is calculated based on at least a second sensor measurement, wherein the second composite value is an indication of a likelihood that the patient's heart failure status has changed. If the second composite value is outside of a second specified range, then an alert of change in heart failure status is generated.