Feline CKD Risk Classification Using Multi-Biomarker Segmentation
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Solution Overview
Problem
There is a need for effective methods to predict, prevent, and reduce the risk of chronic kidney disease (CKD) in felines, as current methods are inadequate for early detection and prevention.
Innovation Solution
A system and method using a processor and memory to analyze biomarkers such as urine specific gravity, creatinine, urine protein, blood urea nitrogen, white blood cell count, and urine pH, through a classification algorithm developed from a training dataset, to determine the susceptibility and risk of CKD in felines, providing a customized recommendation for dietary regimens and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional staging methods based on fasting blood creatinine concentration are used, then the staging process is simple and quick, but the detection precision and early prediction capability are insufficient
Solution Approach 1:
The patent segments the CKD assessment into multiple independent biomarker measurements (urine specific gravity, urine protein, blood urea nitrogen, creatinine, white blood cell count, urine pH) rather than relying on a single creatinine measurement. Each biomarker is measured and analyzed separately, then integrated to provide a comprehensive risk assessment, thereby improving detection precision without overwhelming complexity
Solution Approach 2:
The testing system is designed to perform multiple functions: it measures various biomarkers, stages CKD according to IRIS guidelines, predicts future CKD risk, and provides customized recommendations. This multi-functional approach consolidates what would otherwise require multiple separate testing protocols into a single integrated system
2Reliability
If multiple biomarkers are measured and analyzed through classification algorithms, then the CKD risk prediction accuracy is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system employs classification algorithms that automatically process the collected biomarker data and generate CKD risk predictions without requiring manual interpretation. The algorithm self-adjusts based on training data and provides standardized output, reducing the need for complex manual analysis while maintaining high prediction reliability
Solution Approach 2:
The system incorporates feedback mechanisms where the classification algorithm continuously refines its predictions based on measured biomarker levels and known outcomes. This feedback loop improves prediction reliability over time while the algorithm manages its own complexity through iterative learning rather than requiring increasingly complex manual processing
3Loss of time
If early detection methods are implemented, then the treatment timing is improved, but the testing frequency and monitoring requirements increase
Solution Approach 1:
The system performs preliminary CKD risk assessment by measuring multiple biomarkers that indicate early stages of kidney disease before significant damage occurs. By detecting subtle changes in urine specific gravity, protein levels, and other biomarkers early, the system enables timely intervention while the biomarkers themselves serve as the detection mechanism, avoiding the need for more invasive or time-consuming follow-up tests
Data Source
AI summary
The presently disclosed subject matter relates to methods of determining a feline's susceptibility to developing chronic kidney disease (CKD) and to methods of preventing and/or reducing a risk of developing CKD for a feline. In certain embodiments, the biomarkers comprise creatinine, urine specific gravity or urea.