Coronary artery disease (CAD) results in blockages to the blood vessels that supply the heart and, if left untreated, can lead to heart attacks and even death. In fact, CAD is the leading cause of death in North America and many other countries. It's important to detect CAD as soon as possible, to improve the chances of a successful treatment.

Medical diagnostics company CardioDX has developed a blood test (called Corus CAD^®) administered by a doctor, which quickly and efficiently indicate the likelihood of CAD. This is the first non-intrusive test for predicting CAD, which would otherwise require electrocardiogram tests or CT scans. It does this by measuring the expression level of 23 genes in the blood, which in turns indicates the presence of CAD. Those 23 genes were selected and validated through sophisticated statistical analysis of over 10 gigabytes of genomic data from thousands of patients.

R (and specifically Revolution R Enterprise) had a significant role in the statistical analysis that supports the Corus CAD test. Michael Elashoff, Director of Biostatistics at CardioDX, said the following in a press release issued today:

Analyzing clinical data from thousands of patients poses a serious analytical challenge. We rely on R for nearly all of our research projects because it allows us to run accurate and effective analyses. Revolution R offers even greater speed than open source R and allowed us to run analyses for the Corus CAD^® faster than we could with any alternative. This was especially beneficial for us considering the fact that it took over one million separate analyses to develop this algorithm.

You can read more about how CardioDX used Revolution R to develop the Corus CAD test in this case study or in the complete press release linked below.

Revolution Analytics: CardioDX uses Revolution Analytics to develop first non-intrusive test for predicting coronary artery disease