Exercise electrocardiography for pre-test assessment of the likelihood of coronary artery disease

ObjectivesTo develop a tool including exercise electrocardiography (ExECG) for patient-specific clinical likelihood estimation of patients with suspected obstructive coronary artery disease (CAD). MethodsAn ExECG-weighted clinical likelihood (ExECG-CL) model was developed in a training cohort of patients with suspected obstructive CAD undergoing ExECG. Next, the ExECG-CL model was applied in a CAD validation cohort undergoing ExECG and clinically driven invasive coronary angiography and a prognosis validation cohort and compared with the risk factor-weighted clinical likelihood (RF-CL) model for obstructive CAD discrimination and prognostication, respectively.In the CAD validation cohort, obstructive CAD was defined as >50% diameter stenosis on invasive coronary angiography. For prognosis, the endpoint was non-fatal myocardial infarction and death. ResultsThe training cohort consisted of 1214 patients (mean age 57 years, 57% males). In the CAD (N=408; mean age 55 years, 53% males) and prognosis validation (N=3283; mean age 57 years, 57% males) cohorts, 11.8% patients had obstructive CAD and 4.4% met the endpoint. In the CAD validation cohort, discrimination of obstructive CAD was similar between the ExECG-CL and RF-CL models: area under the receiver-operating characteristic curves 83.1% (95% CIs 77.5% to 88.7%) versus 80.7% (95% CI 74.6% to 86.8%), p=0.14. In the ExECG-CL model, more patients had very low (& LE;5%) clinical likelihood of obstructive CAD compared with the RF-CL (42.2% vs 36.0%, p<0.01) where obstructive CAD prevalence and event risk remained low. ConclusionsExECG incorporated into a clinical likelihood model improves reclassification of patients to a very low clinical likelihood group with very low prevalence of obstructive CAD and favourable prognosis.

Automated summary

This study aims to develop a tool using exercise electrocardiography (ExECG) to estimate the clinical likelihood of patients with suspected obstructive coronary artery disease (CAD). The study found that incorporating ExECG into a clinical likelihood model can reclassify patients into a very low clinical likelihood group, with low prevalence of obstructive CAD and favorable prognosis.