Clinical risk factors alone are inadequate for predicting significant coronary artery disease

被引：3

作者：

Korley, Frederick K. ^{[1
]}

Gatsonis, Constantine ^{[2
,3
]}

Snyder, Bradley S. ^{[4
]}

George, Richard T. ^{[5
]}

Abd, Thura ^{[6
]}

Zimmerman, Stefan L. ^{[7
]}

Litt, Harold I. ^{[8
,9
]}

Hollander, Judd E. ^{[10
]}

机构：

[1] Univ Michigan, Dept Emergency Med, Med Sch, North Campus Res Bldg,026-333N,2800 Plymouth Rd, Ann Arbor, MI 48105 USA

[2] Brown Univ, Sch Publ Hlth, Ctr Stat Sci, Providence, RI 02912 USA

[3] Brown Univ, Sch Publ Hlth, Dept Biostat, Providence, RI 02912 USA

[4] Brown Univ, Sch Publ Hlth, Ctr Stat Sci, Providence, RI 02912 USA

[5] Johns Hopkins Univ, Sch Med, Div Cardiol, Dept Internal Med,Adjunct Fac, Baltimore, MD USA

[6] Johns Hopkins Univ, Sch Med, Div Cardiol, Dept Internal Med, Baltimore, MD USA

[7] Johns Hopkins Univ, Sch Med, Dept Radiol, Baltimore, MD 21205 USA

[8] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA

[9] Univ Penn, Dept Internal Med, Perelman Sch Med, Div Cardiovasc Med, Philadelphia, PA 19104 USA

[10] Thomas Jefferson Univ, Dept Emergency Med, Philadelphia, PA 19107 USA

来源：

JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY | 2017年 / 11卷 / 04期

关键词：

CT coronary angiography; Cardiac risk factors; Coronary artery disease; Prediction; Emergency department; Modeling; COMPUTED-TOMOGRAPHY ANGIOGRAPHY; EMERGENCY-DEPARTMENT PATIENTS; CHEST-PAIN; CT ANGIOGRAPHY; CARDIOVASCULAR EVENTS; PRETEST PROBABILITY; CALCIUM; SCORE; DISCHARGE; OUTCOMES;

D O I：

10.1016/j.jcct.2017.04.011

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Objective: We sought to derive and validate a model for identifying suspected ACS patients harboring undiagnosed significant coronary artery disease (CAD). Methods: This was a secondary analysis of data from a randomized control trial (RCT). Patients randomized to the CTA arm of an RCT examining a CTA-based strategy for ruling-out acute coronary syndrome (ACS) constitute the derivation cohort, which was randomly divided into a training dataset (2/3, used for model derivation) and a test dataset (1/3, used for internal validation (IV)). ED patients from a different center receiving CTA to evaluate for suspected ACS constitute the external validation (EV) cohort. Primary outcome was CTA-assessed significant CAD (stenosis of >= 50% in a major coronary artery). Results: In the derivation cohort, 11.2% (76/679) of subjects had CTA-assessed significant CAD, and in the EV cohort, 8.2% of subjects (87/1056) had CTA-assessed significant CAD. Age was the strongest predictor of significant CAD among the clinical risk factors examined. Predictor variables included in the derived logistic regression model were: age, sex, tobacco use, diabetes, and race. This model exhibited an area under the receiver operating characteristic curve (ROC AUC) of 0.72 (95% CI: 0.61-0.83) based on IV, and 0.76 (95% CI: 0.70, 0.82) based on EV. The derived random forest model based on clinical risk factors yielded improved but not sufficient discrimination of significant CAD (ROC AUC = 0.76 [95% CI: 0.67 0.85] based on IV). Coronary artery calcium score was a more accurate predictor of significant CAD than any combination of clinical risk factors (ROC AUC = 0.85 [95% CI: 0.76-0.94] based on IV; ROC AUC = 0.92 [95% CI: 0.88-0.95] based on EV). Conclusions: Clinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS patients harboring undiagnosed significant coronary artery disease. (C) 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

引用

页码：309 / 316

页数：8