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


Journal Article

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).

Full Text

Duke Authors

Cited Authors

  • Ibrahim, NE; Januzzi, JL; Magaret, CA; Gaggin, HK; Rhyne, RF; Gandhi, PU; Kelly, N; Simon, ML; Motiwala, SR; Belcher, AM; van Kimmenade, RRJ

Published Date

  • March 7, 2017

Published In

Volume / Issue

  • 69 / 9

Start / End Page

  • 1147 - 1156

PubMed ID

  • 28254177

Pubmed Central ID

  • 28254177

Electronic International Standard Serial Number (EISSN)

  • 1558-3597

Digital Object Identifier (DOI)

  • 10.1016/j.jacc.2016.12.021


  • eng

Conference Location

  • United States