PT  - JOURNAL ARTICLE
AU  - Lee Herman
AU  - James Froelich
AU  - Dino Kanelos
AU  - Robert St. Amant
AU  - May Yau
AU  - Brian Rhees
AU  - Mark Monane
AU  - John McPherson
TI  - Utility of a Genomic-based, Personalized Medicine Test in Patients Presenting With Symptoms Suggesting Coronary Artery Disease
AID  - 10.3122/jabfm.2014.02.130155
DP  - 2014 Mar 01
TA  - The Journal of the American Board of Family
                Medicine
PG  - 258--267
VI  - 27
IP  - 2
4099  - http://www.jabfm.org/content/27/2/258.short
4100  - http://www.jabfm.org/content/27/2/258.full
SO  - J Am Board Fam Med2014 Mar 01; 27
AB  - Purpose: Better methods are needed to assess patients presenting with symptoms suggestive of obstructive coronary artery disease (CAD). We hypothesized that the use of a gene expression score (GES) would lead to a change in the diagnostic evaluation. Methods: The Primary Care Providers Use of a Gene Expression Test in Coronary Artery Disease Diagnosis (IMPACT-PCP) trial (clinical trial identifier NCT01594411, clinicaltrials.gov) was a prospective study of stable, nonacute, nondiabetic patients presenting with chest pain and related symptoms at 4 primary care practices. All patients underwent GES testing, with clinicians documenting their planned diagnostic strategy both before and after GES. The GES was derived from a peripheral blood draw measuring expression of 23 genes and has been shown to have a 96% negative predictive value for excluding the diagnosis of obstructive CAD. Results: Of the 251 study patients, 140 were women (56%); the participants had a mean age of 56 years (standard deviation, 13.0) and a mean body mass index of 30 mg/kg2 (standard deviation, 6.7). The mean GES was 16 (range, 1–38), and 127 patients (51%) had a low GES ([ltqeu]15). A change in the diagnostic testing pattern before and after GES testing was noted in 145 of 251 patients (58% observed vs. 10% predefined expected change; P < .001). Conclusions: Incorporation of the GES into the diagnostic workup showed clinical utility above and beyond conventional clinical factors by optimizing the patient's diagnostic evaluation.