Dataset Information

New insights into fibrous cap thickness of vulnerable plaques assessed by optical coherence tomography.

ABSTRACT:

Objective

Vulnerable plaques with fibrous cap thickness (FCT) of ≤65 μm are prone to rupture and/or thrombosis. However, plaques with FCT > 65 μm cause acute myocardial infarction and even sudden death. We aimed to investigate the relationship between 65 < FCT ≤ 80 μm and plaque rupture and/or thrombosis using optical coherence tomography (OCT).

Methods

OCT was performed on culprit lesions in 502 consecutively enrolled patients to identify FCT. Patients were classified into three groups according to FCT: Group A (FCT ≤ 65 μm, n = 147), Group B (65 < FCT ≤ 80 μm, n = 84) and Group C (FCT > 80 μm, n = 271). Clinical and laboratory data was collected from the inpatient medical record system.

Results

Plaques with thinner FCT, especially < 65 μm, were more susceptible to rupture and/or thrombosis (P < 0.001). Plaques with FCT between 65 and 80 μm had a higher probability of rupture and/or thrombosis than those with FCT > 80 μm (P < 0.001). In multivariable analysis, FCT ≤ 65 μm and 65 < FCT ≤ 80 μm were independent predictors for plaque rupture ([FCT ≤ 65 μm vs. FCT > 80 μm]: OR = 8.082, 95% CI = 4.861 to 13.435, P < 0.001; [65 < FCT ≤ 80 μm vs. FCT > 80 μm]: OR = 2.463, 95% CI = 1.370 to 4.430, P = 0.003), thrombosis ([FCT ≤ 65 μm vs. FCT > 80 μm]: OR = 25.224, 95% CI = 13.768 to 46.212, P < 0.001; [65 < FCT ≤ 80 μm vs. FCT > 80 μm]: OR = 3.675, 95% CI = 2.065 to 6.542, P < 0.001) and plaque rupture with thrombosis ([FCT ≤ 65 μm vs. FCT > 80 μm]: OR = 22.593, 95% CI = 11.426 to 44.674, P < 0.001; [65 < FCT ≤ 80 μm vs. FCT > 80 μm]: OR = 4.143, 95% CI = 1.869 to 9.184, P < 0.001).

Conclusions

OCT-assessed 65 < FCT ≤ 80 μm was independently associated with increased risk of plaque rupture and/or thrombosis compared with FCT > 80 μm.

SUBMITTER: Liu X

PROVIDER: S-EPMC9655862 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

New insights into fibrous cap thickness of vulnerable plaques assessed by optical coherence tomography.

Liu Xianglan X He Wujian W Hong Xulin X Li Duanbin D Chen Zhezhe Z Wang Yao Y Chen Zhaoyang Z Luan Yi Y Zhang Wenbin W

BMC cardiovascular disorders 20221112 1

<h4>Objective</h4>Vulnerable plaques with fibrous cap thickness (FCT) of ≤65 μm are prone to rupture and/or thrombosis. However, plaques with FCT > 65 μm cause acute myocardial infarction and even sudden death. We aimed to investigate the relationship between 65 < FCT ≤ 80 μm and plaque rupture and/or thrombosis using optical coherence tomography (OCT).<h4>Methods</h4>OCT was performed on culprit lesions in 502 consecutively enrolled patients to identify FCT. Patients were classified into ...[more]

PMID: 36371146

Similar Datasets

Project description:Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Project description:Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Project description:BackgroundThe risk factors for coronary and carotid plaque development are consistent. Coronary plaque rupture is a major cause of adverse cardiovascular events. Ultrasound can evaluate vulnerable carotid plaques and provide information for predicting vulnerable coronary plaques identified by optical coherence tomography (OCT). This study aimed to investigate the predictive role of non-invasive carotid ultrasound in OCT diagnosis of atherosclerotic vulnerable plaque.MethodsA total of 70 participants, including 35 patients with and 35 without vulnerable coronary plaque, were enrolled in this case-control study at Beijing Anzhen Hospital from 2016 to 2021. The data of 70 patients with suspected coronary heart disease who had undergone OCT examination during percutaneous coronary intervention (PCI) surgery and completed carotid ultrasound examination within 3 days before PCI were analyzed retrospectively. According to the OCT diagnostic criteria for vulnerable plaques, the patients were divided into the vulnerable-plaque group and the stable-plaque group. Univariate and binary logistic regression analyses assessed risk factors for vulnerable coronary plaque. Receiver operating characteristic (ROC) curve analysis was used to determine the predictive power of carotid plaque features.ResultsThe univariate analysis demonstrated that the differences in high-sensitivity C-reactive protein levels and carotid plaque characteristics (irregular fibrous cap, heterogeneous plaque, hypoechoic plaque, plaque calcification, and a plaque thickness of greater than 3 mm) between the two groups were statistically significant. The logistic multivariate regression analysis revealed that an irregular fibrous cap of a carotid plaque [odds ratio (OR) =4.819; 95% confidence interval (CI): 1.106-22.867; P=0.048] and a hypoechoic plaque (OR =9.632; 95% CI: 2.138-43.384; P<0.05) were independent risk factors for predicting vulnerable plaques of the coronary artery.ConclusionsNoninvasive carotid ultrasound is feasible and clinically valuable for predicting vulnerable and asymptomatic coronary plaques defined by OCT. With this method, adverse events can be diagnosed and treated in advance.

Dataset Information

New insights into fibrous cap thickness of vulnerable plaques assessed by optical coherence tomography.

Objective

Methods

Results

Conclusions

Publications

New insights into fibrous cap thickness of vulnerable plaques assessed by optical coherence tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets