Feline CKD Risk Prediction via Multi-Biomarker Classification
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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 treatment.
Innovation Solution
A computer-implemented system that analyzes biomarkers such as urine specific gravity, creatinine, blood urea nitrogen, white blood cell count, and urine pH, using a classification algorithm developed from a training dataset to determine the susceptibility of felines to CKD and provide customized recommendations for dietary regimens or further monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current staging methods based on blood creatinine concentration are used, then CKD can be detected at certain stages, but early susceptibility and risk prediction remain insufficient
Solution Approach 1:
The patent applies preliminary action by using a classification algorithm to predict CKD susceptibility before actual disease manifestation. The system analyzes multiple biomarkers (urine specific gravity, creatinine, urine protein, BUN, WBC, urine pH) and age data to identify cats at risk prior to clinical diagnosis, enabling early intervention before significant kidney damage occurs.
Solution Approach 2:
The patent introduces an intermediary classification algorithm that mediates between raw biomarker data and clinical diagnosis. This algorithm processes multiple input biomarkers and age information to generate risk predictions, serving as an intermediate step that bridges routine laboratory testing with CKD diagnosis and enables earlier detection than traditional methods.
2Reliability
If multiple biomarkers are analyzed using a classification algorithm, then risk prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex risk assessment into distinct components: individual biomarker measurements (urine specific gravity, creatinine, urine protein, BUN, WBC, urine pH), age data, and a classification algorithm that processes these segments. This modular approach manages complexity while improving prediction reliability through comprehensive multi-parameter analysis.
Solution Approach 2:
The patent implements universality through a multi-functional classification algorithm that handles multiple biomarkers and age data within a single integrated system. The algorithm serves multiple purposes: predicting CKD susceptibility, staging risk levels, and enabling early intervention decisions, thereby improving reliability without proportionally increasing complexity.
3Duration of action of moving object
If early detection methods are implemented, then treatment timing improves, but diagnostic capability remains insufficient for susceptibility identification
Solution Approach 1:
The patent applies preliminary action by detecting CKD susceptibility markers before actual kidney disease develops. The classification algorithm analyzes biomarker patterns that indicate increased risk, allowing identification of susceptible cats prior to clinical manifestation, thereby extending the disease-free duration through early preventive intervention.
Solution Approach 2:
The patent replaces traditional mechanical diagnostic approaches with an information-processing system. Instead of relying solely on physical examination and single-point laboratory values, the system uses a classification algorithm that processes multiple biomarker measurements and age data to detect susceptibility patterns, enhancing detection capability for early-stage risk identification.
Data Source
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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.