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

Frontiers in Medicine

Ersoy Subasi¹ 

关键词: chronic kidney disease;    biomarker;    proteomics;    glomerular filtration rate;    proteinuria;    combinatorics;    Boolean;    logical analysis of data;   

DOI  :  10.3389/fmed.2017.00097

学科分类：医学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY