CKD Risk Screening Algorithm Weighting Strategy
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Solution Overview
Problem
Existing methods for predicting the risk of chronic kidney disease (CKD) are limited by their reliance on clinical data from idealized settings, which may not accurately represent real-world populations, and often focus on diabetic nephropathy progression, missing early detection and not accounting for real-world data completeness or veracity.
Innovation Solution
A computer-implemented method that receives marker data including age, creatinine, and albumin levels, weighting age higher than albumin and creatinine higher than albumin to determine a risk factor for CKD, using equations that incorporate constants for accurate prediction, and optionally includes glomerular filtration rate, Body Mass Index, glucose, and HbA1c levels, allowing for early risk assessment in real-world settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical study-based algorithms are used for CKD risk prediction, then prediction accuracy is improved in idealized settings, but applicability to real-world populations deteriorates
Solution Approach 1:
The patent changes the data source parameter from idealized clinical study data to real-world electronic health record data, allowing the model to adapt to diverse population characteristics and real-world variability while maintaining prediction accuracy through appropriate feature selection and weighting
2Measurement precision
If focus is placed on diabetic nephropathy progression, then detailed progression prediction is improved, but early detection capability deteriorates
Solution Approach 1:
The patent incorporates baseline measurements and early-stage markers into the prediction model, enabling risk assessment before significant progression occurs. The model uses electronic health record data capture at any point in time to predict future CKD risk, allowing preliminary identification of at-risk individuals before nephropathy progression becomes apparent
3Measurement precision
If complete feature sets are required for risk prediction, then prediction accuracy is improved, but data completeness in real-world settings deteriorates
Solution Approach 1:
The patent extracts and utilizes only the most critical and commonly available features from electronic health records, such as baseline characteristics and key laboratory values. This selective extraction approach maintains prediction accuracy while accommodating the incomplete and variable data nature of real-world settings, avoiding requirements for comprehensive feature sets that are rarely available
Data Source
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AI summary
The disclosure relates to a method for screening a subject for the risk of chronic kidney disease (CKD), comprising: receiving marker data indicative for a plurality of marker parameters for a subject, such plurality of marker parameters indicating, for the subject for a measurement period, an age value, a sample level of creatinine, and a sample level of albumin, and determining a risk factor indicative of the risk of suffering CKD for the subject from the plurality of marker parameters, wherein the determining comprises weighting the age value higher than the sample level of albumin, and weighting the sample level of creatinine higher than the sample level of albumin. Further, a computer-implemented method for screening a subject and a method for screening a subject are provided.