Steven Rosenberg, PhD; Michael R. Elashoff, PhD; Philip Beineke, BS; Susan E. Daniels, PhD; James A. Wingrove, PhD; Whittemore G. Tingley, MD, PhD; Philip T. Sager, MD; Amy J. Sehnert, MD; May Yau, MS; William E. Kraus, MD; L. Kristin Newby, MD; Robert S. Schwartz, MD; Szilard Voros, MD; Stephen G. Ellis, MD; Naeem Tahirkheli, MD; Ron Waksman, MD; John McPherson, MD; Alexandra Lansky, MD; Mary E. Winn, BS; Nicholas J. Schork, PhD; Eric J. Topol, MD; for the PREDICT (Personalized Risk Evaluation and Diagnosis in the Coronary Tree) Investigators
Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD.
To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients.
Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617)
39 centers in the United States.
An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography.
Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography.
The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond–Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond–Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold.
Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded.
A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD.
Rosenberg S, Elashoff MR, Beineke P, et al, for the PREDICT (Personalized Risk Evaluation and Diagnosis in the Coronary Tree) Investigators. Multicenter Validation of the Diagnostic Accuracy of a Blood-Based Gene Expression Test for Assessing Obstructive Coronary Artery Disease in Nondiabetic Patients. Ann Intern Med. 2010;153:425–434. doi: 10.7326/0003-4819-153-7-201010050-00005
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Published: Ann Intern Med. 2010;153(7):425-434.
Cardiac Diagnosis and Imaging, Cardiology, Coronary Heart Disease.
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