A Classification Model to Predict the Rate of Decline of Kidney Function - Subasi, Ersoy; Subasi, Munevver Mine; Hammer, Peter L.; Roboz, John; Anbalagan, Victor; Lipkowitz, Michael S. (Frontiers in Medicine, 2017)
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.