Background
Diagnosis of significant coronary artery disease (CAD) in at risk
patients can be challenging, typically including non-invasive imaging
modalities and ultimately the gold standard of coronary angiography.
Previous studies suggested that peripheral blood gene expression can reflect
the presence of CAD.
Objective
To validate a previously developed 23-gene expression-based
classifier for diagnosis of obstructive CAD in non-diabetic patients.
Design
Multi-center prospective trial with blood samples drawn prior to
coronary angiography.
Setting
Thirty-nine US centers.
Patients
An independent validation cohort of 526 non-diabetic patients
clinically-indicated for coronary angiography
Intervention
None.
Measurements
Receiver-operator characteristics (ROC) analysis of classifier score
measured by real-time polymerase chain reaction (RT-PCR), additivity to
clinical factors, and reclassification of patient disease likelihood vs
disease status defined by quantitative coronary angiography (QCA).
Obstructive CAD defined as ≥50% stenosis in ≥1 major
coronary artery by QCA.
Results
The overall ROC curve area (AUC) was 0.70 ±0.02,
(p<0.001); the classifier added to clinical variables
(Diamond-Forrester method) (AUC 0.72 with classifier vs 0.66 without, p =
0.003). Net reclassification was improved by the classifier over
Diamond-Forrester and an expanded clinical model (both p<0.001). At
a score threshold corresponding to 20% obstructive CAD likelihood
(14.75), the sensitivity and specificity were 85% and 43%,
yielding NPV of 83% and PPV 46%, with 33% of patient
scores below this threshold.
Limitations
The study excluded patients with chronic inflammatory disorders,
elevated white blood counts or cardiac protein markers, and diabetes.
Conclusions
This non-invasive whole blood test, based on gene expression and
demographics, may be useful for assessment of obstructive CAD in
non-diabetic patients without known CAD.
Primary Funding Source
CardioDx, Inc.