A Classification Model to Predict the Rate of Decline of Kidney Function
| Autor: | Michael S. Lipkowitz, Peter L. Hammer, Munevver Mine Subasi, Victor Anbalagan, Ersoy Subasi, John Roboz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Pathology African american population logical analysis of data 0211 other engineering and technologies Renal function 02 engineering and technology Disease Cross-validation 03 medical and health sciences 0302 clinical medicine Logical analysis of data proteomics Internal medicine medicine 030212 general & internal medicine Original Research glomerular filtration rate lcsh:R5-920 021103 operations research Proteinuria business.industry General Medicine medicine.disease 3. Good health combinatorics Cardiology Biomarker (medicine) Medicine biomarker Boolean medicine.symptom proteinuria business lcsh:Medicine (General) chronic kidney disease Kidney disease |
| Zdroj: | Frontiers in Medicine, Vol 4 (2017) Frontiers in Medicine |
| DOI: | 10.3389/fmed.2017.00097/full |
| Popis: | The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology. On cross-validation by 10-folding, the model was shown to have an accuracy of 80.6 ± 0.11%, sensitivity of 78.4 ± 0.17%, and specificity of 78.5 ± 0.16%. The LAD discriminant is used to identify the patients in different risk groups. The LAD risk scores assigned to 116 AASK patients generated a receiver operating curves curve with AUC 0.899 (CI 0.845–0.953) and outperforms the risk scores assigned by proteinuria, one of the best predictors of chronic kidney disease progression. |
| Databáze: | OpenAIRE |
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