CKD Progression Prediction Using Uric Acid Velocity Models
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Solution Overview
Problem
Existing methods for predicting chronic kidney disease progression are unreliable, require prolonged follow-up, and rely on scarce or expensive resources, lacking timely and robust indicators for early intervention.
Innovation Solution
A system using serial laboratory measurements of uric acid and HbA1c levels, combined through multivariable mathematical models like logistic regression or neural networks, to predict CKD progression up to 36 months in advance, providing leading indicators for preventive interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing prediction methods are used, then CKD progression can be predicted, but the prediction is unreliable and requires prolonged follow-up
Solution Approach 1:
The patent applies preliminary action by using serial laboratory measurements taken before CKD progression occurs to predict future disease stages. The system analyzes historical data patterns (such as uric acid velocity, HbA1c trends, and other biomarkers) to forecast progression risk 36 months in advance, eliminating the need for prolonged actual follow-up while maintaining prediction reliability through multivariable mathematical modeling.
2Reliability
If existing prediction methods are used, then CKD progression can be predicted, but scarce or expensive resources are required
Solution Approach 1:
The patent employs cheap short-living objects by utilizing routinely available, inexpensive laboratory tests (such as serum uric acid, HbA1c, creatinine, and electrolyte measurements) that can be performed in standard clinical settings. These common biomarkers are measured serially over time using standard laboratory equipment, replacing the need for expensive specialized resources while maintaining prediction reliability through the accumulation and analysis of temporal data patterns.
3Loss of time
If timely prediction of CKD progression is achieved, then preventive interventions can be initiated, but robust indicators are lacking
Solution Approach 1:
The patent applies composite materials by creating a composite prediction model that integrates multiple laboratory measurements (uric acid velocity, HbA1c, creatinine, electrolytes, demographics) into a unified risk assessment. This composite approach combines diverse data sources and temporal patterns to generate a robust prediction indicator that is both timely and reliable, overcoming the limitation of single-marker approaches.
4Measurement precision
If multivariable mathematical models are used, then prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming raw laboratory measurements into derived parameters such as uric acid velocity (rate of change over time), HbA1c trends, and eGFR trajectories. These transformed parameters capture temporal dynamics and are fed into the multivariable model, improving prediction accuracy while managing complexity through focused selection of clinically relevant derived metrics rather than raw data processing.
Data Source
AI summary
Systems, methods and computer-readable media are provided for identifying patients having an elevated near-term risk of chronic kidney disease (CKD) progression, including predicting an individual's risk of progression to Stage 3 CKD within a future time interval, which may be up to 36 months. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In an embodiment, measurements of physiological variables are obtained, including serial measurements for uric acid levels from a longitudinal time series of serum or plasma samples spanning the previous two to five years. An annualized uric acid velocity of the patient is determined and used to generate a multivariable mathematical model for determining a likelihood of risk for developing Stage 3 CKD within 36 months.