Hasil untuk "Diseases of the circulatory (Cardiovascular) system"

Koen G. Van der kooy, H. V. van Hout, H. Marwijk et al.

Richard J. Jones

Sascha Hatahet, Sorin Popescu, Charlotte Eitel et al.

BackgroundA novel balloon-in-basket pulsed field ablation (BiB-PFA) catheter enables efficient pulmonary vein isolation (PVI) and allows posterior wall isolation (PWI) within the same procedure. The incremental biological effect of PWI compared to PVI alone remains uncertain, particularly regarding inflammation, myocardial injury, and hemolysis.MethodsIn this prospective, single-center study, consecutive patients with atrial fibrillation underwent first-time BiB-PFA, either PVI only or PVI plus PWI. Venous blood samples were collected before and one day after ablation. Biomarkers included leukocytes, platelets, hemoglobin, C-reactive protein (CRP), haptoglobin, bilirubin, lactate dehydrogenase (LDH), creatinine, estimated glomerular filtration rate (GFR), myoglobin, creatine kinase (CK), and troponin T.ResultsA total of 60 patients were enrolled (PVI only n = 30, PVI + PWI n = 30). Baseline characteristics were comparable. PVI + PWI required more applications (19 vs. 16; p < 0.001) but had similar procedure time. Both groups showed significant increases in inflammatory (CRP, leukocytes), myocardial (troponin T, CK, LDH, myoglobin), and hemolysis markers (bilirubin, LDH, haptoglobin changes; all p < 0.001). However, the magnitude of biomarker release did not differ between PVI only and PVI + PWI: Δ troponin T (1,154 vs. 1,029 ng/L, p = 0.694), Δ CK (217 vs. 197 U/L, p = 0.652), Δ CRP (2.7 vs. 3.4 mg/L, p = 0.475), Δ bilirubin (2.4 vs. 2.8 µmol/L, p = 0.842), Δ creatinine (3.3 vs. 9.0 µmol/L, p = 0.085).ConclusionBiB-PFA PVI provokes systemic responses involving inflammation, myocardial injury, and hemolysis. Adjunctive PWI increases application number but does not further increase biomarker release, supporting the biological safety of PWI.

Zhongjian Tang, Yang Song, Min Zhang et al.

Clinical management of massive pulmonary embolism is challenging when active hemorrhage, a contraindication to thrombolytics, is concurrently present. We describe a successful attempt in using low-dose catheter-directed thrombolysis (CDT) in a high-risk patient with absolute contraindications to systemic thrombolysis. A 69-year-old Asian female with cardiac arrest was brought to a resource-limited rural hospital. The patient underwent 50 min of cardiopulmonary resuscitation (CPR) before regaining the pulse but remained in cardiogenic shock. Computerized tomography (CT) of the chest found massive PE. The patient was found with multiple fractures and subarachnoid hemorrhage. Catheter-directed embolectomy was performed without clinical improvement. A low-dose CDT with alteplase was attempted by giving 5 mg over 2 h with a repeated session 24 h later for a total of 10 mg. The patient started improving, was extubated on Day 9, and transferred out of the ICU on Day 15. Low-dose CDT in massive PE could be lifesaving despite the presence of absolute alteplase contraindications. Patients with contraindications, a high risk of bleeding, or of Asian race may benefit more from the low-dose alteplase regimen.

Theodorus Dapamede, Aisha Urooj, Vedant Joshi et al.

Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. In this retrospective study of 116,135 women from two healthcare systems, a transformer-based neural network quantified BAC severity (no BAC, mild, moderate, and severe) on screening mammograms. Outcomes included major adverse cardiovascular events (MACE) and all-cause mortality. BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22), moderate (HR 1.38-1.47), to severe BAC (HR 2.03-2.22) across datasets (all p<0.001). This association remained significant across all age groups, with even mild BAC indicating increased risk in women under 50. BAC remained an independent predictor when analyzed alongside ASCVD risk scores, showing significant associations with myocardial infarction, stroke, heart failure, and mortality (all p<0.005). Automated BAC quantification enables opportunistic cardiovascular risk assessment during routine mammography without additional radiation or cost. This approach provides value beyond traditional risk factors, particularly in younger women, offering potential for early CVD risk stratification in the millions of women undergoing annual mammography.

Yuanlin Mo, Haishan Huang, Bocheng Liang et al.

Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.

Ajay Mittal, Raghav Mehta, Omar Todd et al.

Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challenge, in this work, we break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification. Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas. These are further utilized to extract clinically interpretable radiomic features as well as deformation field based geometric features (through atlas registration) for CVD classification. Our experiments on the publicly available ASOCA dataset show that utilizing these features leads to better CVD classification accuracy (87.50\%) when compared against classification model trained directly on raw CT images (67.50\%). Our code is publicly available: https://github.com/biomedia-mira/grc-net

Trusting Inekwe, Winnie Mkandawire, Emmanuel Agu et al.

The COVID-19 pandemic disrupted healthcare systems worldwide, disproportionately impacting individuals with chronic conditions such as cardiovascular disease (CVD). These disruptions -- through delayed care and behavioral changes, affected key CVD biomarkers, including LDL cholesterol (LDL-C), HbA1c, BMI, and systolic blood pressure (SysBP). Accurate modeling of these changes is crucial for predicting disease progression and guiding preventive care. However, prior work has not addressed multi-target prediction of CVD biomarker from Electronic Health Records (EHRs) using machine learning (ML), while jointly capturing biomarker interdependencies, temporal patterns, and predictive uncertainty. In this paper, we propose MBT-CB, a Multi-target Bayesian Transformer (MBT) with pre-trained BERT-based transformer framework to jointly predict LDL-C, HbA1c, BMI and SysBP CVD biomarkers from EHR data. The model leverages Bayesian Variational Inference to estimate uncertainties, embeddings to capture temporal relationships and a DeepMTR model to capture biomarker inter-relationships. We evaluate MBT-CT on retrospective EHR data from 3,390 CVD patient records (304 unique patients) in Central Massachusetts during the Covid-19 pandemic. MBT-CB outperformed a comprehensive set of baselines including other BERT-based ML models, achieving an MAE of 0.00887, RMSE of 0.0135 and MSE of 0.00027, while effectively capturing data and model uncertainty, patient biomarker inter-relationships, and temporal dynamics via its attention and embedding mechanisms. MBT-CB's superior performance highlights its potential to improve CVD biomarker prediction and support clinical decision-making during pandemics.

Frederike Lübeck, Jonas Wildberger, Frederik Träuble et al.

Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the challenges of real-world clinical practice as they oversimplify patient profiles, rely on rigid input schemas, and are sensitive to distribution shifts. We developed AdaCVD, an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank. In benchmark comparisons, AdaCVD surpasses established risk scores and standard machine learning approaches, achieving state-of-the-art performance. Crucially, for the first time, it addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data. In stratified analyses, it demonstrates robust performance across demographic, socioeconomic, and clinical subgroups, including underrepresented cohorts. AdaCVD offers a promising path toward more flexible, AI-driven clinical decision support tools suited to the realities of heterogeneous and dynamic healthcare environments.

Sampad Sengupta, Emily Manchester, Jie Wang et al.

Analysing the haemodynamics of flow in carotid artery disease serves as a means to better understand the development and progression of associated complex diseases. Carotid artery disease can predispose people to major adverse cardiovascular events. Understanding the nature of carotid blood flow using in silico methods enables the extraction of relevant metrics that are not accessible in vivo. This study develops computationally efficient means of modelling patient-specific flow, utilising 3D tomographic ultrasound to generate anatomically faithful reconstructions including artherosclerotic plaque, and computational fluid dynamics to simulate flow in these arteries. A computationally efficient model has been proposed here, which has been used to conduct simulations for a large dataset, the results of which where stastitically analysed to test the association of relevant haemodynamic metrics with cardiovascular events. The incidence of major cardiovascular diseases in carotid artery disease patients has been shown to have an association with flow vorticity in the region of interest, and less so with the absolute magnitudes of wall shear stress.

Qasam M. Ghulam, Jens P. Goetze, Nikolaj Eldrup et al.

Introduction: Abdominal aortic aneurysms (AAAs) with intraluminal thrombus (ILT) are suggested to be more prone to rupture than AAAs without. Prior studies indicate that the von Willebrand factor (vWf) plays a role in the formation of ILT since a positive correlation between ILT volume and vWf has been shown. vWf mediates the tethering of platelets at sites of endothelial injury, and the protease ADAMTS-13 cleaves larger forms of vWf, thus counteracting the thrombosis cascade and maintaining haemostatic balance. When investigating the largest quantifiable thrombus in the human body, it was hypothesised that circulating ADAMTS-13 activity may be associated with ILT size in patients with AAA and the aim was to explore this potential relationship using 3D contrast enhanced ultrasound (3D-CEUS) for ILT volume determination. Report: In this retrospective, exploratory study, 60 patients with AAA were evaluated, and the association between ILT volume and thickness and ADAMTS-13 was estimated using 3D-CEUS. ADAMTS-13 activity was measured in plasma samples obtained the same day. No association between ILT volume (r = −0.03, p = 0.84) or ILT thickness (r = 0.02, p = 0.87) and ADAMTS-13 activity was found. Likewise, when subdividing the group into lowest and highest 50% of ADAMTS-13 activity, the half with the lowest ADAMTS-13 activity (mean ILT volume ±standard deviation [SD]: 32 ± 34 mL) did not differ from the half with the highest ADAMTS-13 activity (43 ± 24 mL) when comparing ILT volume (p = 0.172, F = 2.95) and thickness (p = 0.070). Discussion: After evaluating the largest quantifiable intraluminal thrombus in the vasculature, it was concluded that, surprisingly, circulating ADAMTS-13 activity seems unrelated to ILT formation in AAA.

Mirajul Islam, Michael J. Daniels, Zeynab Aghabazaz et al.

Cardiovascular disease (CVD) cohorts collect data longitudinally to study the association between CVD risk factors and event times. An important area of scientific research is to better understand what features of CVD risk factor trajectories are associated with the disease. We develop methods for feature selection in joint models where feature selection is viewed as a bi-level variable selection problem with multiple features nested within multiple longitudinal risk factors. We modify a previously proposed Bayesian sparse group selection (BSGS) prior, which has not been implemented in joint models until now, to better represent prior beliefs when selecting features both at the group level (longitudinal risk factor) and within group (features of a longitudinal risk factor). One of the advantages of our method over the BSGS method is the ability to account for correlation among the features within a risk factor. As a result, it selects important features similarly, but excludes the unimportant features within risk factors more efficiently than BSGS. We evaluate our prior via simulations and apply our method to data from the Atherosclerosis Risk in Communities (ARIC) study, a population-based, prospective cohort study consisting of over 15,000 men and women aged 45-64, measured at baseline and at six additional times. We evaluate which CVD risk factors and which characteristics of their trajectories (features) are associated with death from CVD. We find that systolic and diastolic blood pressure, glucose, and total cholesterol are important risk factors with different important features associated with CVD death in both men and women.

Qihan Hu, Daomiao Wang, Hong Wu et al.

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.

Minfeng Xu, Chen-Chen Fan, Yan-Jie Zhou et al.

Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preventive interventions remains an unsolved challenge in preventive cardiology. Current CVD risk prediction models, recommended by guidelines, are based on limited traditional risk factors or use CT imaging to acquire quantitative biomarkers, and still have limitations in predictive accuracy and applicability. On the other hand, end-to-end trained CVD risk prediction methods leveraging deep learning on CT images often fail to provide transparent and explainable decision grounds for assisting physicians. In this work, we proposed a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our approach initiated with a deep CVD risk classification model by capturing comprehensive continuous deep learning features while jointly obtaining currently clinical-established quantitative biomarkers via segmentation models. In the feature joint representation stage, we use an instance-wise feature-gated mechanism to align the continuous and discrete features, followed by a soft instance-wise feature interaction mechanism fostering independent and effective feature interaction for the final CVD risk prediction. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker, which is important in assisting physicians' decision-making processes. We validated our method on a public chest low-dose CT dataset and a private external chest standard-dose CT patient cohort of 17,207 CT volumes from 6,393 unique subjects, and demonstrated superior predictive performance, achieving AUCs of 0.875 and 0.843, respectively.

Zhimin Yang, Yanyu Li, Zihua Wang

Exosomes are naturally occurring nanoscale vesicles that are released and received by almost all cells in the body. Exosomes can be transferred between cells and contain various molecular constitutes closely related to their origin and function, including proteins, lipids, and RNAs. The importance of exosomes in cellular communication makes them important vectors for delivering a variety of drugs throughout the body. Exosomes are ubiquitous in the circulatory system and can reach the site of injury or disease through a variety of biological barriers. Due to its unique structure and rich inclusions, it can be used for the diagnosis and treatment of diseases. Mesenchymal stem-cell-derived exosomes (MSCs-Exo) inherit the physiological functions of MSCs, including repairing and regenerating tissues, suppressing inflammatory responses, and regulating the body’s immunity; therefore, MSCs-Exo can be used as a natural drug delivery carrier with therapeutic effects, and has been increasingly used in the treatment of cardiovascular diseases and neurodegenerative diseases. Here, we summarize the research progress of MSCs-Exo as drug delivery vectors and their application for various drug deliveries, providing ideas and references for the study of MSCs-Exo in recent years.

Liu Miao, Yan-Li Liu

Cardiovascular disease is one of the major diseases threatening the health of Chinese residents, and the death rate has long been the highest on the disease spectrum in China. With the progress of population aging, the prevalence and mortality of cardiovascular diseases remain on the rise, and the current treatment effect on and prognosis of heart failure (HF) are not satisfactory. It is particularly important to explore the potential pathogenic mechanisms of HF and identify new therapeutic targets.

Daegwang Yoo, Minsun Kang, Jaehun Jung

Background Postpancreatectomy diabetes can be caused by resection of functioning pancreatic tissue and is associated with postoperative pancreatic islet cell mass loss and subsequent endocrine dysfunction. Diabetes is a well‐known risk factor for ischemic heart disease. However, no previous studies have investigated ischemic heart disease in patients with postpancreatectomy diabetes and pancreatic cancer. Methods and Results Rates of patients with diabetes diagnosed with pancreatic cancer who underwent pancreatectomy between 2002 and 2019 in South Korea were obtained from the Korean National Health Insurance Service database. Patient‐level propensity score matching was conducted to reduce the possibility of selection bias, and multivariate Cox proportional hazards models were used to determine the association between postpancreatectomy diabetes and ischemic heart disease. In total, 30 242 patients were initially enrolled in the study. After applying exclusion criteria and propensity score matching, 2952 patients were included in the comparative analysis between the postpancreatectomy group with diabetes and the group without diabetes. Patients in the postpancreatectomy group with diabetes had significantly higher rates of ischemic heart disease than those in the group without diabetes. In total, 3432 patients were included in the comparison between the postpancreatectomy and prepancreatectomy groups with diabetes. There was no significant difference in the risk of ischemic heart disease between the postpancreatectomy and prepancreatectomy groups with diabetes. Conclusions Patients who developed diabetes after pancreatectomy had a higher risk of ischemic heart disease than patients who did not develop diabetes after pancreatectomy, and the rate of ischemic heart disease in these patients was similar to that in patients preoperatively diagnosed with diabetes.

Cheng Guo, Sajid Ahmed, Mohamed-Slim Alouini

The state-of-the-art cardiovascular disease diagnosis techniques use machine-learning algorithms based on feature extraction and classification. In this work, in contrast to a conventional single Electrocardiogram (ECG) lead, two leads are used, and autoregressive (AR) coefficients and statistical parameters are extracted to be used as features. Four machine-learning classifiers support-vector-machine (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and Naive Bayes are applied on these features to test the accuracy of each classifier. For simulation, data is collected from the MIT-BIH and Shaoxing Peoples Hospital China (SPHC) database. To test the generalization ability of our proposed methodology machine-learning model is built on the SPHC database and tested on the MIT-BIH database and self-collected datasets. In the single-database simulation, the MLP performs better than the other three classifiers. While in the cross-database simulation, the SVM-based model trained by the SPHC database shows superiority. For normal and LBBB heartbeats, the predicted recall respectively reaches 100% and 98.4%. Simulation results show that the performance of our proposed methodology is better than the state-of-the-art techniques for the same database. While for cross-database simulation, the results are promising too. Finally, in the demonstration of our realized system, all heartbeats collected from healthy people are classified as normal beats.

P. Franczuk, M. Tkaczyszyn, Maria Kulak et al.

Viral respiratory infections (VRI) are the most prevalent type of infectious diseases and constitute one of the most common causes of contact with medical care. Regarding the pathophysiology of the cardiovascular system, VRI can not only exacerbate already existing chronic cardiovascular disease (such as coronary artery disease or heart failure) but also trigger new adverse events or complications (e.g., venous thromboembolism), the latter particularly in subjects with multimorbidity or disease-related immobilization. In the current paper, we provide a narrative review of diverse cardiovascular complications of VRI as well as summarize available data on the pathology of the circulatory system in the course of coronavirus disease 2019 (COVID-19).

X. Chang, Tian Zhang, Wenjin Zhang et al.

Oxidative stress (OS) refers to the physiological imbalance between oxidative and antioxidative processes leading to increased oxidation, which then results in the inflammatory infiltration of neutrophils, increased protease secretion, and the production of a large number of oxidative intermediates. Oxidative stress is considered an important factor in the pathogenesis of cardiovascular disease (CVD). At present, active components of Chinese herbal medicines (CHMs) have been widely used for the treatment of CVD, including coronary heart disease and hypertension. Since the discovery of artemisinin for the treatment of malaria by Nobel laureate Youyou Tu, the therapeutic effects of active components of CHM on various diseases have been widely investigated by the medical community. It has been found that various active CHM components can regulate oxidative stress and the circulatory system, including ginsenoside, astragaloside, and resveratrol. This paper reviews advances in the use of active CHM components that modulate oxidative stress, suggesting potential drugs for the treatment of various CVDs.