Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1446640
Yan Xi, Yi Xu, Zheng Shu
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Abstract

Objective: This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).

Methods: A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.

Results: The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466-11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003-1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387-1.918).

Conclusion: AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.

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高血压对 2 型糖尿病患者冠状动脉斑块和 FFR-CT 的影响:利用人工智能处理冠状动脉计算机断层扫描血管造影进行评估。
研究目的本研究利用人工智能(AI)量化冠状动脉计算机断层扫描(CCTA)图像,旨在比较有或无高血压(HTN)的 2 型糖尿病(T2DM)患者的斑块特征和 CT 导出的分数血流储备(FFR-CT):我们对在一个中心接受 CCTA 检查的 1,151 名疑似冠状动脉疾病患者进行了回顾性分析。患者被分为 T2DM(n = 133)、HTN(n = 442)、T2DM(HTN+)(n = 256)和对照组(n = 320)。AI 评估了各种 CCTA 参数,包括斑块成分、高危斑块 (HRP)、FFR-CT、使用冠状动脉疾病报告和数据系统 2.0 (CAD-RADS 2.0) 的冠状动脉狭窄严重程度、节段受累评分 (SIS) 和节段狭窄评分 (SSS)。统计分析比较了各组的这些参数:结果:T2DM(高血压+)组的斑块体积和长度、SIS、SSS 和 CAD-RADS 2.0 分级最高。在 T2DM 组中,54.0% 的斑块体积为非钙化,46.0% 为钙化,而在 HTN 组中,这两个数值分别为 24.0% 和 76.0%。T2DM(HTN+)组的钙化斑块(35.7%为非钙化,64.3%为钙化)多于T2DM组。T2DM 组的平均坏死核心体积为 4.25 立方毫米,T2DM(HTN+)组为 5.23 立方毫米,两者无显著差异(P > 0.05)。与 HTN 组和对照组相比,HRP 在 T2DM 组和 T2DM(HTN+)组中更为普遍(P P 3,P 结论:对 CCTA 的 AI 分析确定了定量斑块特征和 FFR-CT 值的模式。合并高血压会加重部分钙化斑块,导致 T2DM 患者冠状动脉狭窄更加严重。T2DM与部分非钙化斑块有关,而高血压与部分钙化斑块有关。
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CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography. Using large language models to support pre-service teachers mathematical reasoning-an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry. Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing. Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost. Heuristic machine learning approaches for identifying phishing threats across web and email platforms.
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