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

Ruosen Yuan, Yao Zhao, He Yuan et al.

Abstract Background The COLCOT trial showed that patients with diabetes may benefit from low-dose colchicine, suggesting a potential interplay between insulin resistance (IR) and inflammation. Whether their combined assessment improves mortality risk stratification in the general population remains unclear. Methods We analyzed 50,654 adults from NHANES 1999–2018 linked to the National Death Index. IR and inflammation were assessed using estimated glucose disposal rate (eGDR) and the log₂-transformed aggregate index of systemic inflammation (AISI), respectively. Survey-weighted Cox proportional hazards models were used for all-cause mortality. For cardiovascular (CVD) mortality, cumulative incidence functions (CIFs) were estimated with Gray’s test for between-group comparisons, and Fine–Gray subdistribution hazard models were fitted treating non-CVD death as a competing event. Discrimination was assessed using time-dependent ROC curves at 5 and 10 years. Robustness was evaluated through sensitivity analyses excluding immune-modifying conditions/treatments, applying a 24-month lag, and excluding extreme absolute lymphocyte counts. Results Over a median follow-up of 120 months, 6,936 all-cause deaths and 2,170 CVD deaths occurred. Higher eGDR was inversely associated with mortality (all-cause HR per 1-unit increase 0.90, 95% CI 0.88–0.92; CVD sHR 0.88, 95% CI 0.85–0.91), whereas higher log₂(AISI) was positively associated (all-cause HR per doubling 1.10, 95% CI 1.06–1.15; CVD sHR 1.13, 95% CI 1.06–1.20). In joint analyses, participants with low eGDR (≤ 8.40) and high log₂(AISI) (> 7.98) had the highest risks of all-cause mortality (HR 1.58, 95% CI 1.38–1.81) and CVD mortality (cause-specific HR 2.09, 95% CI 1.58–2.77; Fine–Gray sHR 2.13, 95% CI 1.66–2.74), with graded separation of CIFs (Gray’s test P < 0.001). The combined model showed improved discrimination (AUCs at 5/10 years: all-cause 0.705/0.723; CVD 0.754/0.769). Results were consistent across sensitivity analyses. Conclusion In a nationally representative U.S. cohort, eGDR and log₂(AISI) were independently and jointly associated with all-cause and CVD mortality. Their combined assessment improves risk stratification and may help identify individuals most likely to benefit from targeted preventive and anti-inflammatory strategies. Graphical abstract

Lucas R. Mareque, Ricardo L. Armentano, Leandro J. Cymberknop

Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.

Weike Zhao, Chaoyi Wu, Yanjie Fan et al.

Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exceeding five years, marked by repeated referrals, misdiagnoses, and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burdens. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support powered by large language models, integrating over 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured Human Phenotype Ontology terms, and genetic testing results, to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 3,134 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next-best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser's 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.

Davide Borghini, Davide Marchi, Angelo Nardone et al.

As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-encoders to efficiently address these challenges. We apply our methodology to a clinical dataset from patients with ischaemic heart disease. Patient data is embedded in a latent space, built using unlabelled data, which is then used to train a neural network classifier to predict cardiovascular death. Results show improved balanced accuracy compared to applying the classifier directly to the raw data, demonstrating that this solution is promising, especially in conditions where availability of unlabelled data could increase.

Weixin Sun, Weixin Sun, Peijie Li et al.

BackgroundIschemic heart disease (IHD) remains a global public health challenge. This study explores global trends in IHD burden among youths and young adults aged 15–39 years from 1990 to 2021.MethodsData were obtained from the 2021 Global Burden of Disease (GBD) study. Estimated annual percentage change was used to assess trends in age-standardized prevalence rate (ASPR), incidence rate (ASIR), mortality rate (ASMR), and disability-adjusted life years (DALYs). Risk factors were analyzed globally and by socio-demographic index (SDI) regions. Bayesian age-period-cohort models predicted trends over the next 30 years.ResultsFrom 1990 to 2021, IHD-related mortality and DALYs declined overall, while prevalence and incidence increased. The largest increases in ASPR, ASIR, ASMR, and DALY rates were observed in middle-SDI regions. Geographically, Asia bore the heaviest burden, whereas high-income North America showed the greatest decreases in prevalence and incidence. In 2021, Oceania had the highest IHD-related deaths and DALYs, with Lesotho exhibiting the greatest rise in ASMR and DALY rates. The IHD burden rose with age, peaking in the 35–39 years group, and was higher in males. Major risk factors included high low-density lipoprotein cholesterol, smoking, and high systolic blood pressure. Projections suggest a global decline in IHD burden, with decreasing incidence and deaths across both sexes by 2050.ConclusionsWhile mortality and DALYs have decreased over the past 30 years, prevalence and incidence of IHD in youths and young adults have increased. The burden is projected to decline, emphasizing the need for targeted interventions, particularly in males aged 35–39 years, based on regional epidemiological patterns and risk factors.

S. N. Tereshchenko, E. B. Yarovaya, S. G. Leontiev et al.

Aim. To evaluate the efficacy and safety of single bolus administration of non-immunogenic staphylokinase in comparison with placebo in patients with intermediatehigh risk pulmonary embolism (PE) within the FORPE-2 clinical trial.Material and methods. Non-immunogenic staphylokinase has high thrombolytic activity and fibrin selectivity. The FORPE-2 clinical trial has a multicenter, doubleblind, randomized, placebo-controlled design. In clinical sites, patients (486 in total, with a possible 10% dropout rate) with confirmed PE and evidence of right ventricular dysfunction based on computed tomography pulmonary angiography and an increased risk of hemodynamic instability (intermediate-high-risk PE) will be equally randomized into two groups to receive non-immunogenic staphylokinase or placebo. The study protocol provides inclusion and exclusion criteria, calculation of the required patient sample size, and the study plan. The primary efficacy endpoint will be a composite of all-cause mortality, hemodynamic collapse, and recurrent PE within 30 days of randomization. Safety endpoints will be hemorrhagic stroke during hospitalization and BARC type 3 and 5 bleeding types.Results. The study will provide data on the efficacy and safety of non-immunogenic staphylokinase in patients with intermediate-high risk PE. A report will be compiled with individual data and statistical analysis of the results.

Konstantin Krieger, MD, Innu Park, MD, Thomas Kemper, MD et al.

Background: Cardiac sympathetic denervation as a treatment for drug-refractory ventricular arrhythmias (VAs) involves video-assisted thoracoscopic removal of the stellate ganglion (SG) and thoracic ganglia. A simplified approach sparing the SG and targeting left T2–T4 ganglia (left cardiac sympathetic denervation [LCSD]) may offer a less invasive alternative. Objective: This study aimed to evaluate the efficacy and safety of simplified SG-sparing LCSD as a bailout procedure for multimorbid patients with structural heart disease and recurrent VAs refractory to antiarrhythmic drugs and/or catheter ablation. Methods: All patients undergoing SG-sparing LCSD at our institution between June 2023 and June 2024 were included in this single-center retrospective study. Baseline demographics, procedural complications, and arrhythmia outcomes were analyzed. Results: LCSD was performed in 7 patients (mean age 75.9 ± 6.7 years, mean LVEF 30.7 ± 10.9%) with structural heart disease (nonischemic cardiomyopathy, n = 3; ischemic cardiomyopathy, n = 4) mostly 1 day (interquartile range 1–21) after admission with a procedure duration of 20.7 ± 5.3 minutes. Initially, 4 patients (57.1%) had electrical storm. Apart from 1 pleural effusion requiring drainage, no major complications or Horner’s syndrome occurred. During a follow-up of 7 ± 2.6 months, median VA episodes requiring implantable cardioverter-defibrillator therapy decreased from 14 to 2 (P = .021) and median implantable cardioverter-defibrillator shocks from 1.5 to 0 (P = .034). Three patients remained free of sustained VAs; 1 patient died of coronavirus disease 2019. Conclusion: In this case series of 7 patients, SG-sparing LCSD demonstrated promising results in terms of safety and efficacy for reducing VAs. Further studies are warranted to confirm long-term outcomes with this approach.

Gaofeng Wang, Qiqiang Jie, Kailing Xu et al.

BackgroundPatients with atrial fibrillation (AF) and atrial septal defect (ASD) face elevated thromboembolic risks, yet evidence on combined left atrial appendage closure (LAAC) and ASD closure remains limited. We aimed to assess the feasibility and safety of a “one-stop” strategy for simultaneous LAAC and ASD closure.MethodsA retrospective analysis included 40 patients with non-valvular AF and ASD undergoing combined procedures (2016–2024). Procedural success, complications, and long-term outcomes (mean follow-up: 1,194.3 days) were analyzed.ResultsAll procedures were technically successful. No major complications (stroke, device embolization, or death) occurred during follow-up. Peri-device leak (PDL) was observed in 19 patients (47.5%), with only one case of device-related thrombus (resolved with anticoagulation).ConclusionThe “one-stop” approach is a safe and effective strategy for stroke prevention in AF patients with ASD, particularly those unsuitable for long-term anticoagulation.

Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

Hanqing Dai, Wenqing Dai, Yuanyuan Chen et al.

Manufacturing hydrogels with identical electrochemical properties are typically riddled with unresolved inquiries and challenges. Here, we utilized ultra-light graphene flakes to trace the influence of convection phenomena during reactions on hydrogels' formation and structural non-uniformity, elucidating its mechanisms. Furthermore, we confirmed that an external electric field induced the orientation of functional groups of hydrogels along the direction of this field, revealing the mechanism of its influence on the structural non-uniformity and electrochemical properties of hydrogels. Additionally, we discovered that ion diffusion was "Stumbling-to-Fetters" by the functional groups on the polymer chains within the hydrogel, unveiling this mechanism and developing the Virginia Creeper (VC) model for hydrogels. We demonstrated the scalability and application of the VC model. Furthermore, we proposed a molecular-ion diffusion and current decay equation to describe the electrochemical properties of hydrogels. As an application of the VC model, we developed a bionic cardiovascular system and proved its potential to seamlessly interface with living organisms and generate bio-like bioelectricity. Our findings provide novel insights into triboelectricity and guidance for producing hydrogels with identical electrochemical properties, and offer a new pathway for bioelectric generation and the design of new hydrogel devices.

Pratham Mehta, Harsha Karanth, Haoyang Yang et al.

Surgical planning for congenital heart diseases requires a collaborative approach, traditionally involving the 3D-printing of physical heart models for inspection by surgeons and cardiologists. Recent advancements in mobile augmented reality (AR) technologies have offered a promising alternative, noted for their ease-of-use and portability. Despite this progress, there remains a gap in research exploring the use of multi-user mobile AR environments for facilitating collaborative cardiovascular surgical planning. We are developing ARCollab, an iOS AR application designed to allow multiple surgeons and cardiologists to interact with patient-specific 3D heart models in a shared environment. ARCollab allows surgeons and cardiologists to import heart models, perform gestures to manipulate the heart, and collaborate with other users without having to produce a physical heart model. We are excited by the potential for ARCollab to make long-term real-world impact, thanks to the ubiquity of iOS devices that will allow for ARCollab's easy distribution, deployment and adoption.

Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena et al.

The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational Fluid Dynamics (CFD) is a commonly used tool for patient-specific simulations in the cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LV). The formation of an asymmetric vortex in a healthy LV shows an efficient way of blood transport. The vortex pattern changes before any change in the geometry of LV is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that understanding vortex dynamics in LV using various vortex indexes coupled with data-driven approaches can be used as an early diagnosis tool and improvement in DCM treatment.

Omer Belhasin, Idan Kligvasser, George Leifman et al.

Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. ECG sensing is non-invasive and relatively easy to acquire, and yet it is still cumbersome for holter monitoring tests that may span over hours and even days. A possible alternative in this context is Photoplethysmography (PPG): An optically-based signal that measures blood volume fluctuations, as typically sensed by conventional ``wearable devices''. While PPG presents clear advantages in acquisition, convenience, and cost-effectiveness, ECG provides more comprehensive information, allowing for a more precise detection of heart conditions. This implies that a conversion from PPG to ECG, as recently discussed in the literature, inherently involves an unavoidable level of uncertainty. In this paper we introduce a novel methodology for addressing the PPG-2-ECG conversion, and offer an enhanced classification of cardiovascular conditions using the given PPG, all while taking into account the uncertainties arising from the conversion process. We provide a mathematical justification for our proposed computational approach, and present empirical studies demonstrating its superior performance compared to state-of-the-art baseline methods.

Gorana Mijatovic, Laura Sparacino, Yuri Antonacci et al.

The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate to infer the underlying network topology. To address these limitations, here we introduce a framework which combines the assessment of high-order interactions with statistical inference for the characterization of the functional links sustaining physiological networks. The framework develops information-theoretic measures quantifying how two nodes interact in a redundant or synergistic way with the rest of the network, and employs these measures for reconstructing the functional structure of the network. The measures are implemented for both static and dynamic networks mapped respectively by random variables and random processes using plug-in and model-based entropy estimators. The validation on theoretical and numerical simulated networks documents the ability of the framework to represent high-order interactions as networks and to detect statistical structures associated to cascade, common drive and common target effects. The application to cardiovascular networks mapped by the beat-to-beat variability of heart rate, respiration, arterial pressure, cardiac output and vascular resistance allowed noninvasive characterization of several mechanisms of cardiovascular control operating in resting state and during orthostatic stress. Our approach brings to new comprehensive assessment of physiological interactions and complements existing strategies for the classification of pathophysiological states.

Balazs C. Lengyel, MD, Jacob B. Watson, MD, Min P. Kim, MD et al.

Pulmonary arteriovenous malformations create continuous shunting of unoxygenated blood through the lungs into the systemic circulation. These malformations are asymptomatic if small, but cause serious symptoms as they grow in size. Treatment primarily consists of endovascular embolization; lobectomy is preserved for recurring or endovascularly untreatable cases. We describe a case of a 24-year-old man who was first treated with coil embolization 10 years previously, with complete symptom resolution. However, more recently he noted recurrent exercise intolerance, with shortness of breath and hypoxemia. After repeat re-embolization, a computed tomography scan noted some persistent flow. Given the patient's young age, we considered resection as a definite therapy. The patient underwent an uncomplicated robot-assisted right lower lobectomy. Afterward, his symptoms resolved completely. In selected cases, robotic lobectomy for pulmonary arteriovenous malformation is feasible and safe.

Samundra Timilsina Tripathee, J. Kafle

Arterial stenosis is the lessening of the arterial wall due to the growth of aberrant tissues that prevent adequate blood flow in the human circulatory system and induces cardiovascular diseases. Mild stenosis, over time, can lead to serious and permanent damage if it remains uncured. Navier-Stokes equation in cylindrical polar coordinates system has been extended by two-layered blood flow along the axial direction with appropriate boundary conditions.A steady flow through a stenosed artery has been investigated extensively using the analytical approach in the case of two-layered blood flow. Mathematical expressions for two-layer hemodynamic parameters such as velocity profile, volumetric flow rate, and effect of stenosis progression on parameters with the variation of core and peripheral layered coefficient of viscosity are derived. Moreover, pressure drop, and shear stress have been calculated analytically in an artery with and without stenosis. Peripheral viscosity has less contribution to varying velocity distribution than the core and is proportional to stenosis size.The volumetric flow rate decreases with an increasing viscosity coefficient. Pressure drop and shear stress attain maximum value in the region stenosis occur maximum height in core layer.The present work could serve as a model in biomedical engineering for the cure of vascular-related diseases and has the potential in designing the devices of this field.

T. Talaieva

ENDOTHELIAL DYSFUNCTION IN THE PATHOGENESIS OF ARTERIAL HYPERTENSION: NEW DIAGNOSTIC METHODS Tetiana V. Talaieva State Institution «National Scientific Center «M. D. Strazhesko Institute of Cardiology, Clinical and Regenerative Medicine of the National Academy of Medical Sciences of Ukraine», 5, Svyatoslava Khorobroho Str., Kyiv, Ukraine 03151 Introduction. Arterial hypertension (AH) remains the most widespread disease of the circulatory system, as well as one of the leading risk factors for the development of cardiovascular diseases. Recent studies indicate that endothelial dysfunction may play a key role in the pathogenesis of hypertension. Endothelial dysfunction is associated with damage and accelerated apoptosis of endothelial cells (ECs), and quite often these changes occur before morphological and clinical signs of the disease appear. The researches of the last decades established that the most important properties of the endothelium - restoration and preservation of structural and functional integrity and its reparative activity are directly related to circulating endothelial progenitor cells - precursor cells of endotheliocytes (ECCs). The use of various markers for the determination of ECCs in circulating blood, the determination of the content of exfoliated endothelial cells and the reserve function of the bone marrow (ability to produce ECCs) makes it possible to assess the function of the endothelium and the risk of the development and progression of cardiovascular diseases. Purpose: using the flow cytometry method to evaluate the possibility of determining the content of ECCs in the blood, desquamated ECs and the reserve function of the bone marrow (ability to produce ECCs) as markers of endothelial dysfunction. Materials and methods. 153 patients with AH were included in the study. All patients underwent complaint registration, history taking, general clinical examination, including office blood pressure measurement and daily blood pressure monitoring, physical examination, brachial artery compression test to assess endothelium-dependent vasodilatation. The number of ECCs of peripheral blood was determined by the method of flow cytometry with the help of reagents for the determination of differentiation clusters CD34, CD45, CD31, CD133 manufactured by "Beckman Coulter Inc.". In the conditions of the test with dosed physical load, the blood content of ECCs was determined on a bicycle ergometer before and after 60 minutes. after completing the test on 55 patients with hypertension. The results. In the studies, complex determination of various markers on the surface of the ECCs was used. At baseline, the number of ECCs (CD34+/CD45-/+) was 22 % lower in patients with hypertension, and 28 % lower in patients with resistant hypertension than in practically healthy donors (р < 0.05). The number of ECCs (CD133+ CD31+ CD45-/+) in patients with hypertension was 25 % less than in the norm. The number of desquamated cells exceeded the norm by 152 % (р < 0.001). In patients with hypertension, there was a decrease in the reserve function of the bone marrow to produce ECCs in response to ischemia caused by stress. The obtained data are confirmed by the results of the brachial artery compression test. Based on the results of the analysis in the groups with controlled and resistant hypertension, it was found that the last index of EDVD was 25 % lower than in the group of patients with hypertension that is well controlled (р < 0.05). The use of standard therapy for 12 weeks was accompanied by an increase in the number of ECCs in patients, which indicated the restoration of endothelial function after the treatment. Conclusions. In patients with hypertension, a decrease in the content of ECCs in the blood was noted. The resistant course of hypertension is associated with greater manifestations of endothelial dysfunction. Determination of ECCs using the flow cytometry method provides important additional information about endothelial dysfunction as a risk factor for the development and progression of hypertension, and can also be used to assess the effectiveness of antihypertensive therapy. Keywords: arterial hypertension, endothelium, dysfunction, endothelial progenitor cells.

Soban Manzoor Soban Manzoor, Waqas Asghar Waqas Asghar, Rao Sanaullah Khan Rao Sanaullah Khan et al.

Pesticide residues on fruits and vegetables are of major health concern around the world. Some of these pesticide residues are extremely toxic and can become a major causative factor for various diseases such as cardiovascular disorders (CVDs), lung, endocrine, and nervous system damage, as well as the circulatory system, and reproductive system problems. This study was aimed at investigating the effectiveness of saponins isolated from alfalfa (Medicago sativa L.) seeds for mitigating cypermethrin residues on apples (Malus domestica Borkh.) in comparison to tap water, citric acid, and baking soda. Cypermethrin concentration applied to apples was 1 ml/L. After washing the apples with varying concentrations of different washing solutions, analysis for cypermethrin residues was performed using a UV/VIS spectrophotometer at a wavelength of 535 nm. The maximal removal of residues recorded for baking soda, tap water, and citric acid was 92.98, 72.50, and 74.59 % respectively. Saponins exhibited a maximum of 13.90 % of residual removal which was not as effective as other washing agents.

Maitham G. Yousif, Hector J. Castro

The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings.

Henrik A. Kjeldsberg, Joakim Sundnes, Kristian Valen-Sendstad

Computational fluid dynamics (CFD) in combination with patient-specific medical images has been used to correlate flow phenotypes with disease initiation, progression and outcome, in search of a prospective clinical tool. A large number of CFD software packages are available, but are typically based on rigid domains and low-order finite volume methods, and are often implemented in massive low-level C++ libraries. Furthermore, only a handful of solvers have been appropriately verified and validated for their intended use. Our goal was to develop, verify and validate an open-source CFD solver for moving domains, with applications to cardiovascular flows. The solver is an extension of the CFD solver Oasis, which is based on the finite element method and implemented using the FEniCS open source framework. The new solver, named OasisMove, extends Oasis by expressing the Navier-Stokes equations in the arbitrary Lagrangian-Eulerian formulation, which is suitable for handling moving domains. For code verification we used the method of manufactured solutions for a moving 2D vortex problem, and for validation we compared our results against existing high-resolution simulations and laboratory experiments for two moving domain problems of varying complexity. Verification results showed that the $L_2$ error followed the theoretical convergence rates. The temporal accuracy was second-order, while the spatial accuracy was second- and third-order using P1/P1 and P2/P1 finite elements, respectively. Validation results showed good agreement with existing benchmark results, by reproducing lift and drag coefficients with less than 1% error, and demonstrating the solver's ability to capture vortex patterns in transitional and turbulent-like flow regimes. In conclusion, we have shown that OasisMove is an open-source, accurate and reliable solver for cardiovascular flows in moving domains.