Acute Phase Response Biomarker Derivative Analysis for Early Sepsis Detection
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Solution Overview
Problem
Current clinical practices rely on C-Reactive Protein (CRP) concentrations to monitor acute phase response post-operatively, but these are not effective in predicting complications following abdominal surgery, with limited predictive accuracy and delayed detection of sepsis, leading to prolonged ICU stays and increased healthcare costs.
Innovation Solution
A method involving the processing of acute phase response biomarker data to determine the time derivative of CRP and other biomarkers, providing early warnings of recovery or complications by analyzing the rate of change, which can be used to adjust alert states and predict patient outcomes up to 24 hours earlier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CRP concentration monitoring is used, then the monitoring method is simple and widely available, but the detection of complications is delayed and predictive accuracy is limited
Solution Approach 1:
The patent transforms the monitoring approach from measuring absolute CRP concentrations to measuring the rate of change (first derivative) and acceleration (second derivative) of CRP levels. This parameter transformation enables earlier detection of complications by identifying changes in the trend of CRP evolution before absolute concentration thresholds are reached, thereby improving both predictive accuracy and reducing detection delay.
Solution Approach 2:
By monitoring the first and second derivatives of CRP time courses, the system performs preliminary detection of complications before they become clinically apparent. The method identifies inflection points and changes in the rate of CRP increase that precede actual complications, allowing for earlier intervention and treatment.
2Measurement precision
If high sensitivity CRP assays are used, then early-time changes in patient recovery can be detected, but the assays are rarely used clinically due to complexity and cost
Solution Approach 1:
The patent introduces mathematical derivatives as an intermediary layer between standard CRP measurements and clinical interpretation. By calculating the first and second derivatives from routinely available CRP time course data, the system extracts early predictive information without requiring complex high-sensitivity assays, thus maintaining simplicity while improving detection capability.
Solution Approach 2:
The invention changes the analytical parameters from absolute concentrations to rates of change and acceleration. This transformation allows standard CRP assays to provide early predictive information that would otherwise require more sensitive and complex testing methods.
3Ease of operation
If absolute CRP concentration thresholds are used for monitoring, then the monitoring approach is straightforward, but the prognostic value is poor and complications are not predicted accurately
Solution Approach 1:
The patent transitions from static threshold-based monitoring to dynamic trend analysis by incorporating first and second derivatives of CRP time courses. This dynamic approach captures the evolution pattern of CRP levels, allowing for more reliable prognostic assessment while maintaining operational simplicity through automated calculation and interpretation algorithms.
Solution Approach 2:
The system implements feedback by continuously monitoring the derivatives of CRP levels and comparing them against expected patterns. Changes in the rate of increase or acceleration provide feedback about the patient's recovery trajectory, enabling more accurate prognostic assessment while keeping the monitoring process straightforward through algorithmic evaluation.
Data Source
AI summary
We describe a method of predicting the response of a patient to a medical procedure, the method comprising: inputting acute phase response (APR) biomarker data defining a level of an acute phase response (APR) biomarker in said patient at a succession of biomarker measurement times following said medical procedure, said APR biomarker data defining a biomarker time course representing an evolution over time of said acute phase response; and processing said APR biomarker data to determine a derivative with respect to time of said time course from said APR biomarker data to provide APR time series data; determining a prediction of the response of said patient to said medical procedure from said APR time series data.