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

TITLE=A Classification Model to Predict the Rate of Decline of Kidney Function

JOURNAL=Frontiers in Medicine

VOLUME=Volume 4 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2017.00097

DOI=10.3389/fmed.2017.00097

ISSN=2296-858X

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  SELDI-TOF proteomic data from  rapid and slow progressors classified by rate of change of GFR. 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 an ROC 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.