Poruban Tibor, Pella Dominik, Schusterova Ingrid, Jakubova Marta, Sieradzka Uchnar Karolina Angela, Barbierik Vachalcova Marianna
East Slovak Institute of Cardiovascular Diseases and School of Medicine, Pavol Jozef Safarik University, Kosice, Slovakia.
Cardiovasc Diagn Ther. 2024 Dec 31;14(6):1029-1037. doi: 10.21037/cdt-24-179. Epub 2024 Dec 16.
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.
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.
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).
The SIC, serving as an additional echocardiography-based marker, shows promise in enhancing AS severity detection.
超声心动图被广泛用于评估主动脉瓣狭窄（AS），但结果可能不一致，导致AS严重程度存在不确定性以及需要进一步诊断。这项回顾性研究旨在评估一种基于超声心动图的新型标志物——信号强度系数（SIC），其在准确识别和量化AS中的钙含量、增强无创诊断方法方面的潜力。
2022年5月至2023年10月期间，对斯洛伐克科希策市东斯洛伐克心血管疾病研究所的112例先前经超声心动图评估为重度的AS病例以及50例轻度或中度AS病例进行回顾性评估。利用ImageJ软件，我们根据主动脉瓣界面处的超声信号强度分布对SIC进行量化。生成像素强度直方图以测量SIC，并将其与超声心动图变量进行比较。为了考虑超声心动图图像中由于采集设置不同而导致的亮度变化（其中最高强度对应于钙），已实施自适应图像二值化。随后，交互式选择并提取包含钙的感兴趣区域（ROI）。此过程能够计算钙像素计数，代表钙的空间量。本研究采用向后消除和逐步技术的多变量逻辑回归。利用受试者操作特征（ROC）曲线评估模型在预测AS严重程度方面的性能并确定最佳切点。
SIC成为AS严重程度的重要预测指标，优势比（OR）为0.021 [95%置信区间（CI）：0.004 - 0.295，P = 0.008]。将SIC与标准超声心动图参数纳入模型显著提高了C统计量/ROC面积，从0.7023提高到0.8083（P = 0.01）。
SIC作为一种基于超声心动图的额外标志物，在增强AS严重程度检测方面显示出前景。
