Towards a more targeted use of elective coronary angiography: identifying key predictors of obstructive coronary artery disease
V Mahmutaj, A Bakalli, X Krasniqi, T Sadriu, S Elezi, J Sejdiu, I Hoxha, D KocinajAbstract
Background
Elective coronary angiography (ECA) is a widely used and reliable method for diagnosing chronic obstructive coronary artery disease (CAD). However, ECA has several disadvantages and limitations, including its invasive nature, radiation exposure, risk of contrast-induced nephropathy, functional limitations, inability to detect microvascular disease, and the requirement for specialized equipment, trained personnel, and hospital facilities. Therefore, indications for ECA should be carefully considered. Evidence from the literature suggests that this procedure is often overused.
Purpose
Therefore, we aimed to identify predictors of obstructive CAD in patients undergoing ECA.
Methods
This was a prospective cross-sectional study that included 128 consecutive patients that underwent ECA in our tertiary medical center. Patients with obstructive CAD were considered those with coronary artery stenosis >50%. We excluded patients with known or acute CAD, heart failure, atrial fibrillation, and/or poor echocardiography window. Statistical analysis was performed using Stata/BE 18 (StataCorp, College Station, TX, USA).
Results
Half of the patients in our study were found to have obstructive CAD. The logistic regression analysis identified several significant predictors of CAD, as seen in the table. Age was significantly associated with CAD both in unadjusted and adjusted models, indicating that each additional year of age increased the odds of CAD. Gender also emerged as a highly significant predictor, with males having much higher odds of CAD compared to females, reflected in an unadjusted an adjusted model. Smoking status was significantly associated with CAD in both models. Body mass index (BMI) had a borderline significant relationship with CAD in the adjusted model, with an OR of 0.886 (95% CI: 0.788–0.996, p=0.043), suggesting a modest protective effect. Diabetes mellitus was significant in the unadjusted model, with an OR of 2.600 (95% CI: 1.161–5.823, p=0.020), though this association was not significant after adjustment. Whereas, left ventricular global longitudinal strain (LVGLS) was a strong predictor in both models, with an OR of 1.385 (95% CI: 1.192-1.608, p<0.0001) in the unadjusted model and OR of 1.26 (95% CI: 1.263-1.054, p=0.012) in the adjusted model.
Conclusions
This study identifies age, male gender, smoking status, LVGLS as independent predictors of obstructive CAD in patients undergoing ECA. These results suggest that integrating clinical risk factors and echocardiographic markers such as LVGLS could enhance risk stratification and improve decision-making regarding the use of ECA. Given the known limitations and potential overuse of ECA, a more targeted approach incorporating these predictors may help optimize patient selection and resource utilization.For image description, please refer to the figure legend and surrounding text.