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A Classification Model to Predict the Rate of Decline of Kidney Function
Ersoy Subasi1, Munevver Mine Subasi2, Peter L Hammer3
1Department of Engineering Systems, Florida Institute of Technology, Melbourne, FL, United States.
Frontiers in Medicine
|August 4, 2017
Summary
This study developed a proteomic analysis method to predict kidney function decline in African Americans with hypertension. The new model accurately identifies patients at risk, outperforming current methods.
Area of Science:
- Nephrology
- Proteomics
- Biostatistics
Background:
- Hypertension-related kidney disease disproportionately affects African Americans.
- Effective therapies for hypertension-related renal disease are limited.
- Predicting kidney function decline is crucial for managing chronic kidney disease (CKD).
Purpose of the Study:
- To develop a pattern-based classification approach to predict kidney function decline.
- To utilize proteomic data from the African American Study of Kidney Disease and Hypertension (AASK) cohort.
- To identify predictive proteomic biomarkers for CKD progression.
Main Methods:
- Employed surface-enhanced laser desorption ionization/time of flight (SELDI-TOF) proteomic analysis.
- Used Logical Analysis of Data (LAD) methodology for pattern-based classification.
- Validated the model using 10-fold cross-validation on 116 AASK patients.
Main Results:
- A classification model using 7 proteomic features accurately predicted kidney function decline.
- The model achieved 80.6% accuracy, 78.4% sensitivity, and 78.5% specificity.
- LAD risk scores outperformed proteinuria in predicting CKD progression (AUC 0.899 vs. unknown).
Conclusions:
- Proteomic profiling combined with LAD offers a novel approach for predicting kidney function decline.
- This method can identify patients at different risk levels for CKD progression.
- The findings support the use of proteomic biomarkers for personalized risk assessment in hypertensive kidney disease.
Keywords:
Booleanbiomarkerchronic kidney diseasecombinatoricsglomerular filtration ratelogical analysis of dataproteinuriaproteomicsMore Related Videos
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