Predictive Model for High-Risk Coronary Artery Disease.

ABSTRACT: BACKGROUND:Patients with high-risk coronary artery disease (CAD) may be difficult to identify. METHODS:Using the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) cohort randomized to coronary computed tomographic angiography (n=4589), 2 predictive models were developed for high-risk CAD, defined as left main stenosis (?50% stenosis) or either (1) ?50% stenosis [50] or (2) ?70% stenosis [70] of 3 vessels or 2-vessel CAD involving the proximal left anterior descending artery. Pretest predictors were examined using stepwise logistic regression and assessed for discrimination and calibration. RESULTS:High-risk CAD was identified in 6.6% [50] and 2.4% [70] of patients. Models developed to predict high-risk CAD discriminated well: [50], bias-corrected C statistic=0.73 (95% CI, 0.71-0.76); [70], bias-corrected C statistic=0.73 (95% CI, 0.68-0.77). Variables predictive of CAD in both models included family history of premature CAD, age, male sex, lower glomerular filtration rate, diabetes mellitus, elevated systolic blood pressure, and angina. Additionally, smoking history was predictive of [50] CAD and sedentary lifestyle of [70] CAD. Both models characterized high-risk CAD better than the Pooled Cohort Equation (area under the curve=0.70 and 0.71 for [50] and [70], respectively) and Diamond-Forrester risk scores (area under the curve=0.68 and 0.71, respectively). Both [50] and [70] CAD was associated with more frequent invasive interventions and adverse events than non-high-risk CAD (all P<0.0001). CONCLUSIONS:In contemporary practice, 2.4% to 6.6% of stable, symptomatic patients requiring noninvasive testing have high-risk CAD. A simple combination of pretest clinical variables improves prediction of high-risk CAD over traditional risk assessments. CLINICAL TRIAL REGISTRATION:URL: https://www.clinicaltrials.gov . Unique identifier: NCT01174550.

SUBMITTER: Jang JJ 

PROVIDER: S-EPMC6368397 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature