An integrated approach to prognosis using protein microarrays and nonparametric methods.
Over the past several years, multivariate approaches have been developed that address the problem of disease diagnosis. Here, we report an integrated approach to the problem of prognosis that uses protein microarrays to measure a focused set of molecular markers and non-parametric methods to reveal non-linear relationships among these markers, clinical variables, and patient outcome. As proof-of-concept, we applied our approach to the prediction of early mortality in patients initiating kidney dialysis. We found that molecular markers are not uniformly prognostic, but instead vary in their value depending on a combination of clinical variables. This may explain why reports in this area aiming to identify prognostic markers, without taking into account clinical variables, are either conflicting or show that markers have marginal prognostic value. Just as treatments are now being tailored to specific subsets of patients, our results show that prognosis can also benefit from a 'personalized' approach.
Knickerbocker
Tanya
T
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
Chen
Jiunn R
JR
Thadhani
Ravi
R
MacBeath
Gavin
G
D015415:Biomarkers / Q000032:analysis*; D016022:Case-Control Studies; D016207:Cytokines / Q000097:blood; D016208:Databases, Factual; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood / Q000401:mortality; D011379:Prognosis; D040081:Protein Array Analysis; D006435:Renal Dialysis; D012189:Retrospective Studies; D018709:Statistics, Nonparametric
D015415:Biomarkers; D016207:Cytokines