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Bioinformatics and Biology Insights

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality

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Bioinformatics and Biology Insights 2015:Suppl. 3 1-10

Original Research

Published on 01 Sep 2015

DOI: 10.4137/BBI.S30172


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Abstract

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient’s health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.



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Bioinformatics and Biology Insights fills a gap in current journals.  Ever more often, bioinformatics and detailed analysis of data creates novel, unexpected insights.  It is good to have a journal which focusses on exactly this aspect of bioinformatics research, putting the biology insights upfront with high respect for the different methods in bioinformatics.
Dr Thomas Dandekar (University of Wurzburg, European Molecular Biology Laboratory, Heidelberg, Germany)
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