Paper Info
Title
A Classification Model to Predict the Rate of Decline of Kidney Function.
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 AUG 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.
Year
DOI
Venue
2016
10.3389/fmed.2017.00097
FRONTIERS IN MEDICINE
Keywords
Field
DocType
chronic kidney disease,biomarker,proteomics,glomerular filtration rate,proteinuria,combinatorics,Boolean,logical analysis of data
Population,Diabetes mellitus,Disease,Renal function,Internal medicine,Cardiology,Proteinuria,Kidney disease,Biomarker (medicine),Cross-validation,Medicine,Pathology
Conference
Volume
ISSN
Citations 
4
2296-858X
1
PageRank 
References 
Authors
0.35
7
6
Name
Order
Citations
PageRank
Ersoy Subasi173.40
Munevver Mine Subasi2194.24
Peter L. Hammer31996288.93
John Roboz410.35
Victor Anbalagan510.35
Michael S Lipkowitz610.69