A Simple Validated Clinical Tool to Predict the Absence of Coronary Artery Disease in Patients With Systolic Heart Failure of Unclear Etiology

Abstract

Coronary artery disease (CAD) is a major cause of systolic heart failure (HF). Identifying CAD as a cause of systolic HF has prognostic and treatment implications. Whether all patients with systolic HF of unclear etiology should undergo coronary angiography has been controversial. We sought to derive and validate a clinical prediction rule to exclude CAD as a cause of systolic HF. A derivation cohort was formed of consecutive patients who had undergone coronary angiography with a primary diagnosis of systolic HF of unclear etiology (ejection fraction <50%). Using multivariate logistic regression analysis, we derived a prediction rule for severe CAD (≥50% diameter stenosis in the left main, 3-vessel CAD, and 2-vessel CAD involving the proximal left anterior descending artery). The diagnostic performance of the defined prediction rule was prospectively validated in a separate cohort recruited from 2 institutions. Of the 124 patients in the derivation cohort, 27% had CAD, including 15% with severe CAD. The independent predictors of severe CAD included diabetes (odds ratio 5.1, p= 0.005), electrocardiographic Q waves or left bundle branch block (odds ratio 3.8, p= 0.02), and ≥2 nondiabetes risk factors: age (men ≥55 or women ≥65years), dyslipidemia, hypertension, and tobacco use (odds ratio 4.8, p= 0.02). Aprediction rule of having ≥1 independent predictor identified 97% of the patients with CAD and 100% of the patients with severe CAD. In the prospective validation cohort of 143 patients, the prediction rule had 98% sensitivity and 18% specificity for CAD but 100% sensitivity for severe CAD. In conclusion, a simple clinical prediction rule can accurately identify patients with CAD and eliminate the need for angiography in a substantial proportion of patients with systolic HF, with potentially significant cost savings and risk avoidance.