Sepsis Condition Classification Using CRT and Vasodilation Data
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Solution Overview
Problem
Existing technologies face challenges in accurately classifying the condition of a subject related to sepsis, particularly in distinguishing between non-shock and warm shock states, due to minimal differences in capillary refill time (CRT) during the early stages of sepsis.
Innovation Solution
A condition classifying device that receives data on blood refilling state after ischemia and physiological parameters affected by vasodilation, using a processor to classify the subject's condition based on both types of data, thereby enhancing classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only blood refilling state after ischemia (CRT) is used for classification, then the measurement is simple, but the classification accuracy in early stage sepsis is poor
Solution Approach 1:
The patent combines multiple physiological parameters (blood refilling state after ischemia/CRT, physiological parameters affected by vasodilation, and level of consciousness) into a comprehensive classification system. This merging of multiple data sources enables accurate detection of warm shock state in early stage sepsis, resolving the contradiction between measurement simplicity and classification accuracy.
Solution Approach 2:
The patent transitions from single-parameter (CRT only) assessment to multi-dimensional assessment by incorporating physiological parameters affected by vasodilation and level of consciousness. This dimensional expansion provides additional discrimination capability to distinguish warm shock state from non-shock state, improving classification accuracy without excessive complexity increase.
2Reliability
If only CRT is monitored, then the system is simple, but the ability to detect warm shock state at early stage is insufficient
Solution Approach 1:
The patent merges CRT monitoring with physiological parameter monitoring affected by vasodilation and level of consciousness assessment. This combination provides reliable detection of warm shock state through complementary information from multiple sources, improving detection reliability while managing system complexity through integrated monitoring.
Solution Approach 2:
The system continuously monitors multiple physiological parameters and provides feedback for classification determination. The processor analyzes changes in physiological parameters affected by vasodilation and level of consciousness alongside CRT to dynamically classify the shock state, enhancing reliability through continuous multi-parameter feedback assessment.
3Measurement precision
If multiple physiological parameters are collected, then the classification accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct functional components: collecting CRT data, collecting physiological parameter data affected by vasodilation, collecting level of consciousness data, and processing these segmented data streams separately before integration for classification. This segmentation reduces overall processing difficulty by handling each parameter type systematically.
Solution Approach 2:
The processor is designed with multi-functionality to handle different types of physiological data (CRT, vasodilation effects, consciousness levels) through a unified classification framework. This universal processing approach improves classification accuracy while managing data processing complexity through a single integrated algorithm that handles multiple parameter types.
Data Source
AI summary
A condition classifying device includes: an input interface configured to receive first data corresponding to a blood refilling state after ischemia of a subject, and second data corresponding to a physiological parameter that exhibits changes caused by vasodilation of the subject; a processor configured to perform a classification of a condition of the subject related to sepsis based on the first data and the second data; and an output interface configured to output a result of the classification.