Abstract
BACKGROUND: Echocardiography is widely used to assess aortic stenosis (AS) but can yield inconsistent results, leading to uncertainty about AS severity and the need for further diagnostics. This retrospective study aimed to evaluate a novel echocardiography-based marker, the signal intensity coefficient (SIC), for its potential in accurately identifying and quantifying calcium in AS, enhancing noninvasive diagnostic methods. METHODS: Between May 2022 and October 2023, 112 cases of AS that were previously considered severe by echocardiography were retrospectively evaluated, as well as a group of 50 cases of mild or moderate AS, both at the Eastern Slovak Institute of Cardiovascular Diseases in Kosice, Slovakia. Utilizing ImageJ software, we quantified the SIC based on ultrasonic signal intensity distribution at the aortic valve's interface. Pixel intensity histograms were generated to measure the SIC, and it was compared with echocardiographic variables. To account for variations in brightness due to differing acquisition settings in echocardiography images (where the highest intensity corresponds to calcium), adaptive image binarization has been implemented. Subsequently, the region of interest (ROI) containing calcium was interactively selected and extracted. This process enables the calculation of a calcium pixel count, representing the spatial quantity of calcium. This study employed multivariate logistic regression using backward elimination and stepwise techniques. Receiver operating characteristic (ROC) curves were utilized to assess the model's performance in predicting AS severity and to determine the optimal cut-off point. RESULTS: The SIC emerged as a significant predictor of AS severity, with an odds ratio (OR) of 0.021 [95% confidence interval (CI): 0.004-0.295, P=0.008]. Incorporating SIC into a model alongside standard echocardiographic parameters notably enhanced the C-statistic/ROC area from 0.7023 to 0.8083 (P=0.01). CONCLUSIONS: The SIC, serving as an additional echocardiography-based marker, shows promise in enhancing AS severity detection.