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To evaluate the prognosis and influencing factors of retinal vein occlusion (RVO) in patients with concomitant carotid artery disease receiving anti-vascular endothelial growth factor (VEGF) treatment. Patients diagnosed with RVO and receiving anti-VEGF treatment were included. Eye and clinical data were collected. The patients were divided into a group with concomitant carotid artery disease (Group A) and a group without concomitant carotid artery disease (Group B). The risk factors affecting the visual prognosis of RVO patients with concomitant carotid artery disease were analyzed. Among 177 eligible patients with RVO, 101 had concomitant carotid artery disease (Group A), while 76 did not (Group B). Group A had a significantly lower treatment effectiveness rate than Group B (P < 0.001). The age and platelet distribution width of Group A were significantly higher than Group B (P < 0.001). Multivariate logistic regression analysis showed that baseline best-corrected visual acuity (BCVA), disorganization of retinal inner layers (DRIL), external limiting membrane (ELM) disruption, and red blood cell distribution width (RDW) were significantly associated with the posttreatment visual prognosis of RVO patients with concomitant carotid artery disease(P < 0.05). RVO patients with concomitant carotid artery disease had a significantly lower treatment effectiveness rate than RVO patients without carotid artery disease. The poor baseline BCVA, DRIL, ELM disruption, and a greater RDW are risk factors for low anti-VEGF treatment efficacy among RVO patients with concomitant carotid artery disease. [ABSTRACT FROM AUTHOR]

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model's performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model's high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes. [ABSTRACT FROM AUTHOR]

Smoking and hypertension are significant risk factors for atherosclerotic carotid artery disease, but also for intracranial aneurysms. The medical results suggest incidental intracranial aneurysms in patients with internal carotid artery (ICA) stenosis arteries. Which led to a study of the comparison between stenosis and aneurysm segments through the artery. A mathematical and numerical model for blood solidification and its rupture in a diseased artery is described in this paper. The interaction between the blood flow and a diseased arterial wall was formulated. The blood flow is formulated as a micropolar incompressible fluid with heat and mass transfer under the control of electro-osmotic and electromagnetic forces through an artery that contains stenosis and aneurysms. The approximation of mild stenosis is used to derive the governing flow equation, which is then solved using a method of finite difference. Particular attention is paid to the impact on axial velocity, flow rate, resistance impedance, and wall shear stress of geometrical parameters of the arterial wall and rheological blood parameters. In the instantaneous pattern of streamlines, the global behavior of blood is also investigated. Finally, the effect of all parameters on the blood flow through artery whose stenosis and aneurysm are investigated as a comparison of both segments. [ABSTRACT FROM AUTHOR]

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of 90 % . To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics. [ABSTRACT FROM AUTHOR]

Photon counting computed tomography (PCCT) represents a paradigm shift from conventional CT imaging, propelled by a new generation of X-ray detectors capable of counting individual photons and measuring their energy. The first part of this narrative review is focused on the technical aspects of PCCT and describes its key advancements and benefits compared to conventional CT but also its limitations. By synthesizing the existing literature, the second part of the review seeks to elucidate the potential of PCCT as a valuable tool for assessing carotid artery disease. Thanks to the enhanced spatial resolution and image quality, PCCT allows for an accurate evaluation of carotid luminal stenosis. With its ability to finely discriminate between different tissue types, PCCT allows for detailed characterization of plaque morphology and composition, which is crucial for assessing plaque vulnerability and the risk of cerebrovascular events. [ABSTRACT FROM AUTHOR]

Objective: Symptomatic carotid artery disease is indicative of an elevated likelihood of experiencing a subsequent stroke, with the morphology of plaque and its specific features being closely linked to the risk of stroke occurrence. Our study based on the characteristics of carotid plaque assessed by optical coherence tomography (OCT), the plaque morphology evaluated by digital subtraction angiography (DSA) and clinical laboratory indicators were combined, develop a combined predictive model to identify symptomatic carotid plaque. Methods: Patients diagnosed with carotid atherosclerotic stenosis who underwent whole-brain DSA and OCT examination at the Affiliated Hospital of Jining Medical University from January 2021 to November 2023 were evaluated. Clinical features, as well as DSA and OCT plaque characteristics, were analyzed for differences between symptomatic and asymptomatic cohorts. An analysis of logistic regression was carried out to identify factors associated with the presence of symptomatic carotid plaque. A multivariate binary logistic regression equation was established with the odds ratio (OR) serving as the risk assessment parameter. The receiver operating characteristic curve was utilized to assess the combined predictive model and independent influencing factors. Results: A total of 52 patients were included in the study (symptomatic: 44.2%, asymptomatic: 55.8%). Symptomatic carotid stenosis was significantly linked to four main factors: low-density lipoprotein-cholesterol >3.36 mmol/L [OR, 6.400; 95% confidence interval (CI), 1.067-38.402; p = 0.042], irregular plaque (OR, 6.054; 95% CI, 1.016-36.083; p = 0.048), ruptured plaque (OR, 6.077; 95% CI, 1.046-35.298; p = 0.048), and thrombus (OR, 6.773; 95% CI, 1.194-38.433; p = 0.044). The combined predictive model generated using four indicators showed good discrimination (Area Under Curve, 0.924; 95% CI, 0.815-0. 979). The p value was <0.05 with 78.26% sensitivity and 93.10% specificity. Conclusion: OCT is valuable in evaluating the plaque characteristics of carotid atherosclerotic stenosis. The combined predictive model comprising lowdensity lipoprotein-cholesterol >3.36 mmol/L, irregular plaque, ruptured plaque, and thrombus could help in the detection of symptomatic carotid plaque. Further research conducted on additional independent cohorts is necessary to confirm the clinical significance of the predictive model for symptomatic carotid plaque. [ABSTRACT FROM AUTHOR]

This document is a collection of abstracts from the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) conference. The abstracts cover a wide range of topics related to interventional radiology procedures, including the use of drug-coated devices for femoropopliteal lesions, different thrombectomy techniques for deep vein thrombosis, thermal ablation procedures for renal cell carcinoma, and embolic agents for bronchial artery embolization. The abstracts provide valuable insights into the advancements and outcomes of interventional radiology procedures for various medical conditions. [Extracted from the article]

Objective • To explore the relationship between Serum amyloid protein A(SAA), lipoprotein-associated Phospholipase A2 (Lp-PLA2) and soluble CD40 ligand (sCD40L) in detecting the stability of carotid Atherosclerosis plaque. Methods • We examined 90 patients admitted to our hospital with acute cerebral infarction from July 2020 to December 2022. Carotid artery ultrasounds were performed for all of them. These patients were then divided into two groups: the stable plaque group (45 cases) and the unstable plaque group (45 cases), based on the ultrasound results. Additionally, we included a control group of 30 healthy individuals from our hospital. We collected fasting blood samples from the patients upon admission and used enzyme-linked immunosorbent assays to measure the mass concentrations of sCD40L, Lp-PLA2, and SAA in their serum. The results of these biomarkers were compared and analyzed to assess potential associations with plaque stability in patients with cerebral infarction. Results • Comparison of general clinical data and laboratory data: except for High-density lipoprotein, there was a statistical difference between the control group and the cerebral infarction group (P < .05), there was no statistical difference in gender, smoking history, drinking history and age (P > .05). Compared with the control group, the mass concentrations of sCD40L, Lp-PLA2, and SAA in patients with stable and unstable plaques increased significantly (P < .05); Compared with the stable plaque group, the mass concentrations of sCD40L, Lp-PLA2, and SAA in unstable plaque patients increased with statistical significance (P < .05). Correlation analysis shows that the mass concentrations of sCD40L, Lp-PLA2, and SAA are positively correlated with the stability of carotid artery plaques. SCD40L, Lp-PLA2 and SAA have certain diagnostic significance in the subject’s working characteristic curve (Receiver operating characteristic) as a marker molecule for the diagnosis of unstable plaque. sCD40L (AUC=0.883) has more diagnostic value than SAA (AUC=0.756) and Lp-PLA2 (AUC=0.826). A binary logistic regression analysis was conducted using the stability of carotid artery plaques as the dependent variable and sCD40L, Lp-PLA2, and SAA as independent variables. The results showed that elevated serum sCD40L, Lp-PLA2, and SAA were independent risk factors for unstable carotid artery plaques (P < .05). Conclusion • The concentrations of sCD40L, Lp-PLA2 and SAA are closely related to the formation and type of carotid Atherosclerosis plaque in patients with acute cerebral infarction. This has potentially important clinical implications for the management and prevention of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Introduction: Carotid atherosclerotic intraplaque hemorrhage (IPH) predicts stroke. Patients with a history of stroke are treated with antiplatelet agents to prevent secondary cardiovascular events. A positive association between previous antiplatelet use and IPH was reported in a cross-sectional analysis. We investigated the changes in IPH over 2 years in patients who recently started versus those with continued antiplatelet use. Methods: In the Plaque at Risk (PARISK) study, symptomatic patients with <70% ipsilateral carotid stenosis underwent carotid plaque magnetic resonance imaging (MRI) at the baseline and after 2 years to determine IPH presence and volume. Participants were categorized into new users (starting antiplatelet therapy following the index event) and continued users (previous use of antiplatelet therapy before the index event). The association between previous antiplatelet therapy and the presence of IPH at baseline MRI was investigated using multivariable logistic regression analysis. The IPH volume change over a period of 2 years, defined as the difference in volume between follow-up and baseline, was investigated in each group with a Wilcoxon signed-rank test. The IPH volume change was categorized as progression, regression, or no change. Using multivariable logistic regression, we investigated the association between new antiplatelet use and (1) newly developed ipsilateral or contralateral IPH and (2) IPH volume progression. Results: A total of 108 patients underwent carotid MRI at the baseline and follow-up. At the baseline, previous antiplatelet therapy was associated with any IPH (OR = 5.6, 95% CI: 1.3–23.1; p = 0.02). Ipsilateral IPH volume did not change significantly during the 2 years in patients who continued receiving antiplatelet agents (86.4 mm3 [18.2–235.9] vs. 59.3 mm3 [11.4–260.3]; p = 0.6) nor in the new antiplatelet users (n = 31) (61.5 mm3 [0.0–166.9] vs. 27.7 mm3 [9.5–106.4]; p = 0.4). Similar results of a nonsignificant change in contralateral IPH volume during those 2 years were observed in both groups (p > 0.05). No significant associations were found between new antiplatelet use and newly developed IPH at 2 years (odds ratio [OR] = 1.0, 95% CI: 0.1–7.4) or the progression of IPH (ipsilateral: OR = 2.4, 95% CI: 0.3–19.1; contralateral: OR = 0.3, 95% CI: 0.01–8.5). Conclusion: Although the baseline association between IPH and previous antiplatelet therapy was confirmed in this larger cohort, the new onset of antiplatelet therapy after transient ischemic attack/stroke was not associated with the newly developed IPH or progression of IPH volume over the subsequent 2 years. Plain Language Summary: Carotid artery narrowing due to an atherosclerotic plaque is responsible for around 20% of stroke. Intraplaque hemorrhage (IPH) predicts stroke. Antiplatelet agents are often given to patients who have had a stroke to prevent further cardiovascular events. A previous study found an association between previous antiplatelet use and IPH at a specific point in time. In this study, researchers investigated whether starting antiplatelet therapy after a stroke or continuing to use antiplatelets had any effect on IPH over a 2-year period. The study included patients with <70% carotid narrowing who had a carotid plaque MRI at the baseline and after 2 years to determine the presence and volume of IPH. Patients were categorized into new users (starting antiplatelet therapy following the stroke) and continued users (already using antiplatelet therapy before the stroke). The presence and the volume change of IPH over 2 years were investigated in each group. The study found that IPH was more present at the baseline when patients used antiplatelet agents before the stroke. However, new antiplatelet use after the stroke did not lead to new IPH or an increase in IPH volume over the subsequent 2 years. There were no significant associations between the new antiplatelet use and newly developed IPH or IPH volume progression. Therefore, while the baseline association between IPH and previous antiplatelet therapy was confirmed in this larger cohort, starting antiplatelet therapy after a stroke did not increase the risk of new IPH or progression of IPH volume over the subsequent 2 years. [ABSTRACT FROM AUTHOR]

Purpose: The preoperative assessment of carotid plaques is necessary to render revascularization safe and effective. The aim of this study is to evaluate the usefulness of chemical exchange saturation transfer (CEST)-MRI, particularly amide proton transfer (APT) imaging as a preoperative carotid plaque diagnostic tool. Methods: We recorded the APT signal intensity on concentration maps of 34 patients scheduled for carotid endarterectomy. Plaques were categorized into group A (APT signal intensity ≥ 1.90 E-04; n = 12) and group B (APT signal intensity < 1.90 E-04; n = 22). Excised plaques were subjected to histopathological assessment and, using the classification promulgated by the American Heart Association, they were classified as intraplaque hemorrhage-positive [type VI-positive (tVI+)] and -negative [no intraplaque hemorrhage (tVI−)]. Results: Of the 34 patients, 22 (64.7%) harbored tVI+- and 12 (35.3%) had tVI− plaques. The median APT signals were significantly higher in tVI+- than tIVI− patients (2.43 E-04 (IQR = 0.98–4.00 E-04) vs 0.54 E-04 (IQR = 0.14–1.09 E-04), p <.001). Histopathologically, the number of patients with tVI+ plaques was significantly greater in group A (100%, n = 12) than group B (45%, n = 22) (p <.01). The number of symptomatic patients or asymptomatic patients with worsening stenosis was also significantly greater in group A than group B (75% vs 36%, p <.01). Conclusion: In unstable plaques with intraplaque hemorrhage and in patients with symptoms or progressive stenosis, the ATP signals were significantly elevated. CEST-MRI studies has the potential for the preoperative assessment of the plaques' characteristics. [ABSTRACT FROM AUTHOR]

The population in the areas neighboring the Semipalatinsk Nuclear Test Site (SNTS) in the eastern region of Kazakhstan faces increased cardiovascular disease (CVD) risk. Previous research has not explored gene polymorphisms related to CVD in this population. Therefore, the present study examines the prevalence of six CVD-associated genotypes in three generations exposed to SNTS radiation. The genotyping of ApoE Leu28 → Pro, AGT Met174 → Thr, AGT Met235 → Thr, eNOS T786 → C, PON1 Gln192 → Arg, and EDN 1 Lys198 → Asn was performed using real-time polymerase chain reaction. The present study encompassed a cohort of 218 participants with a familial history of arterial hypertension and/or carotid artery disease spanning at least three generations. The analysis unveiled significant disparities in the prevalence of ApoE Leu28 → Pro, eNOS T786 → C, and PON1 Gln192 → Arg genotypes across different generations. Furthermore, a substantial variation in the distribution of the eNOS T786 → C genotype was observed between individuals of Kazakh and Russian ethnicities. Nevertheless, no significant discrepancies were detected in the frequencies of the investigated genotypes between genders. Further research in this area is warranted to enhance the understanding of the genetic factors contributing to CVD in the population exposed to radiation from the SNTS. Specifically, future studies should broaden the scope of genetic polymorphisms investigated and include representatives of healthy individuals who have not been exposed to radiation as controls. [ABSTRACT FROM AUTHOR]

Background Reasons for racial disparities in the utilization and outcomes of carotid interventions (carotid endarterectomy (CEA) and carotid artery stenting (CAS)) are not well understood, especially segregation of care associated with carotid intervention. We examined patterns of geographic and provider care segregation in carotid interventions and outcomes. Method We used de-identified Medicare datasets to identify CEA and CAS interventions between January 1, 2016 and December 31, 2019 using validated ICD-10 codes. For patients who underwent carotid intervention, we calculated (1) the proportion of White patients at the hospital, (2) the proportional difference in the proportion of White patients between hospital patients and the county, and (3) provider care segregation by the dissimilarity index for carotid intervention cases. We examined associations between measures of segregation and outcomes. Results Despite higher proportions of Black patients in counties with hospitals that provide carotid intervention, lower proportions of Black patients received intervention. The difference in the proportion of White patients comparing CEA patients to the county race distribution was 0.143 (SD 0.297) at the hospital level (for CAS, 0.174 (0.315)). The dissimilarity index for CEA providers was high, with mean (SD) 0.387 (0.274) averaged across all hospitals and higher among CAS providers at 0.472 (0.288). Black patients receiving CEA and CAS (compared with Whites) had reduced odds of discharge home. Better outcomes (inpatient mortality and 30-day mortality) were independently associated with higher proportion of White CAS patients. Conclusion In this national study with contemporary data on carotid intervention, we found evidence for segregation of care of both CEA and CAS. [ABSTRACT FROM AUTHOR]

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of 90%. To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics.