Medical History for Prognostic Risk Assessment and Diagnosis of Stable Patients with Suspected Coronary Artery Disease

• Type of Material: Article
• Authors: James K. Min, Weill Cornell Medical College; Allison Dunning, Weill Cornell Medical College; Heidi Gransar, Cedars Sinai Medical Center; Stephan Achenbach, University of Erlangen; Fay Y. Lin, New York-Presbyterian Hospital; Mouaz Al-Mallah, Wayne State University; Matthew J. Budoff, University of Erlangen; Tracy Q. Callister, Tennessee Heart & Vascular Institute; Hyuk-Jae Chang, Severance Cardiovascular Hospital; Filippo Cademartiri, Giovanni XXIII Hospital; Kavitha Chinnaiyan, William Beaumont Hospital; Benjamin J. W. Chow, University of Ottawa; Ralph D'Agostino, Boston University; Augustin DeLago, Capitol Cardiology Associates; John Friedman, Cedars Sinai Medical Center; Martin Hadamitzky, Deutsches Herzzentrum Munchen; Joerg Hausleiter, University of Munich; Sean Hayes, Cedars Sinai Medical Center; Phillip Kaufmann, University Hospital Zurich; Gilbert L. Raff, William Beaumont Hospital; Leslee J Shaw, Emory University
• Language: English
• Date: 2015-08-01
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.amjmed.2014.10.031
• Title of Journal or Parent Work: American Journal of Medicine
• ISSN: 0002-9343
• Volume: 128
• Issue: 8
• Start Page: 871
• End Page: 878
• Grant/Funding Information: This study was also funded, in part, by a generous gift from the Dalio Institute of Cardiovascular Imaging (New York, NY); and the Michael Wolk Foundation (New York, NY).; This research was supported by Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176).; Research reported in this publication was supported by the Heart Lung and Blood Institute of the National Institutes of Health under award number 1R01HL115150.
• Keywords: APPROPRIATE USE CRITERIA; CARDIOVASCULAR COMPUTED-TOMOGRAPHY; Medicine, General & Internal; ECHOCARDIOGRAPHY; Diagnosis; NUCLEAR CARDIOLOGY; ANGIOGRAPHY; Coronary artery disease; GUIDELINES; General & Internal Medicine; COLLEGE; Prognosis; Science & Technology; PROBABILITY; AMERICAN-HEART-ASSOCIATION; Life Sciences & Biomedicine; TASK-FORCE
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Objective: To develop a clinical cardiac risk algorithm for stable patients with suspected coronary artery disease based upon angina typicality and coronary artery disease risk factors. Methods: Between 2004 and 2011, 14,004 adults with suspected coronary artery disease referred for cardiac imaging were followed: 1) 9093 patients for coronary computed tomography angiography (CCTA) followed for 2.0 years (CCTA-1); 2) 2132 patients for CCTA followed for 1.6 years (CCTA-2); and 3) 2779 patients for exercise myocardial perfusion scintigraphy (MPS) followed for 5.0 years. A best-fit model from CCTA-1 for prediction of death or myocardial infarction was developed, with integer values proportional to regression coefficients. Discrimination was assessed using C-statistic. The validated model was tested for estimation of the likelihood of obstructive coronary artery disease, defined as ≥50% stenosis, as compared with the method of Diamond and Forrester. Primary outcomes included all-cause mortality and nonfatal myocardial infarction. Secondary outcomes included prevalent angiographically obstructive coronary artery disease. Results: In CCTA-1, best-fit model discriminated individuals at risk of death or myocardial infarction (C-statistic 0.76). The integer model ranged from 3 to 13, corresponding to 3-year death risk or myocardial infarction of 0.25% to 53.8%. When applied to CCTA-2 and MPS cohorts, the model demonstrated C-statistics of 0.71 and 0.77, respectively. Both best-fit (C = 0.76; 95% confidence interval [CI], 0.746-0.771) and integer models (C = 0.71; 95% CI, 0.693-0.719) performed better than Diamond and Forrester (C = 0.64; 95% CI, 0.628-0.659) for estimating obstructive coronary artery disease. Conclusions: For stable symptomatic patients with suspected coronary artery disease, we developed a history-based method for prediction of death and obstructive coronary artery disease.