A Clinical and Biomarker Scoring System to Predict the Presence of Obstructive Coronary Artery Disease

• Published in: Journal of the American College of Cardiology Vol. 69; no. 9; pp. 1147 - 1156
• Main Authors: Ibrahim, Nasrien E., MD, Januzzi, James L., MD, Magaret, Craig A., MS, Gaggin, Hanna K., MD, MPH, Rhyne, Rhonda F., BPharm, MBA, Gandhi, Parul U., MD, Kelly, Noreen, MD, Simon, Mandy L., DNP, FNP-BC, Motiwala, Shweta R., MD, Belcher, Arianna M., MS, van Kimmenade, Roland R.J., MD, PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 07.03.2017; Elsevier Limited
• Subjects: Aged; Angina pectoris; Archives & records; Biomarkers; Biomarkers - blood; Cardiology; Cardiovascular; Cardiovascular disease; Chronic obstructive pulmonary disease; Cohort Studies; Coronary Artery Disease - blood; Coronary Artery Disease - diagnosis; Coronary Artery Disease - etiology; Coronary Stenosis - blood; Coronary Stenosis - diagnosis; Coronary vessels; diagnosis; Female; Heart attacks; Heart failure; Humans; Internal Medicine; Ischemia; Male; Medical imaging; Medical records; Middle Aged; myocardial infarction; negative predictive value; positive predictive value; Predictive Value of Tests; Proteins; ROC Curve; stenosis; positive predictive value; coronary artery disease; myocardial infarction; apolipoprotein C-I; HR; odds ratio; stenosis; AUC; KIM-1; percutaneous coronary intervention; receiver-operating characteristic; NPV; PPV; MI; Apo C-I; computed tomography; OR; CI; CAD; ROC; diagnosis; area under the curve; kidney injury molecule–1; CT; PCI; hazard ratio; confidence interval; negative predictive value
• ISSN: 0735-1097; 1558-3597
• DOI: 10.1016/j.jacc.2016.12.021

Abstract Background Noninvasive models to predict the presence of coronary artery disease (CAD) may help reduce the societal burden of CAD. Objectives From a prospective registry of patients referred for coronary angiography, the goal of this study was to develop a clinical and biomarker score to predict the presence of significant CAD. Methods In a training cohort of 649 subjects, predictors of ≥70% stenosis in at least 1 major coronary vessel were identified from >200 candidate variables, including 109 biomarkers. The final model was then validated in a separate cohort (n = 278). Results The scoring system consisted of clinical variables (male sex and previous percutaneous coronary intervention) and 4 biomarkers (midkine, adiponectin, apolipoprotein C-I, and kidney injury molecule–1). In the training cohort, elevated scores were predictive of ≥70% stenosis in all subjects (odds ratio [OR]: 9.74; p < 0.001), men (OR: 7.88; p <0.001), women (OR: 24.8; p < 0.001), and those with no previous CAD (OR: 8.67; p < 0.001). In the validation cohort, the score had an area under the receiver-operating characteristic curve of 0.87 (p < 0.001) for coronary stenosis ≥70%. Higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 77% sensitivity, 84% specificity, and a positive predictive value of 90% for ≥70% stenosis. Partitioning the score into 5 levels allowed for identifying or excluding CAD with >90% predictive value in 42% of subjects. An elevated score predicted incident acute myocardial infarction during 3.6 years of follow up (hazard ratio: 2.39; p < 0.001). Conclusions We described a clinical and biomarker score with high accuracy for predicting the presence of anatomically significant CAD. (The CASABLANCA Study: Catheter Sampled Blood Archive in Cardiovascular Diseases; NCT00842868 )