A classification model to predict the rate of decline of kidney function

Ersoy Subasi, Munevver Mine Subasi, Peter L. Hammer, John Roboz, Victor Anbalagan, Michael S. Lipkowitz

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Article number97
JournalFrontiers in Medicine
Volume4
Issue numberJUL
DOIs
StatePublished - 2017

Keywords

  • Biomarker
  • Boolean
  • Chronic kidney disease
  • Combinatorics
  • Glomerular filtration rate
  • Logical analysis of data
  • Proteinuria
  • Proteomics

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