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3,693 results for “Cardiovascular”
Expanding the Family Check-Up in Early Childhood to Promote Cardiovascular Health of Mothers and Young Children
ClinicalTrials.gov study NCT05473767. IPD Sharing: YES. Countries: 1. Publications: 2.
A Randomized Study to Evaluate the Effect of an "Inclisiran First" Implementation Strategy Compared to Usual Care in Patients With Atherosclerotic Cardiovascular Disease and Elevated LDL-C Despite Rec
ClinicalTrials.gov study NCT04929249. IPD Sharing: YES. Countries: 1. Publications: 1.
Safety, Tolerability, and Effect of Alirocumab in High Cardiovascular Risk Patients With Severe Hypercholesterolemia Not Adequately Controlled With Conventional Lipid-modifying Therapies (ODYSSEY APPR
ClinicalTrials.gov study NCT02476006. IPD Sharing: YES. Countries: 16. Publications: 1.
Sleep Apnea in TIA/Stroke: Reducing Cardiovascular Risk With Positive Airway Pressure
ClinicalTrials.gov study NCT01446913. IPD Sharing: YES. Countries: 1. Publications: 2.
A Nurse-led Intervention to Extend the Veteran HIV Treatment Cascade for Cardiovascular Disease Prevention
ClinicalTrials.gov study NCT04545489. IPD Sharing: YES. Countries: 1. Publications: 2.
Cardiovascular Risk Reduction Study (Reduction in Recurrent Major CV Disease Events)
ClinicalTrials.gov study NCT01327846. IPD Sharing: YES. Countries: 40. Publications: 18.
Data from: A village doctor-led mobile health intervention for cardiovascular risk reduction in rural China: cluster randomised controlled trial
Open the record for dataset details and reuse information.
Dataset for "Verifying Monte Carlo simulations of diffusion tensor cardiovascular magnetic resonance using a finite volume method"
<p>This dataset contains the results of random walk and finite volume simulations of diffusion in cardiac tissue. The data was used for the work presented at the 8th World Congress of Biomechanics in 2018.</p>
Risk factors for cardiovascular disease (CVD) in adults with type 1 diabetes: findings from prospective real-life T1D exchange registry
<p>Context</p> <p>Cardiovascular disease (CVD) is a major cause of mortality in adults with type 1 diabetes.</p> <p>Objective</p> <p>We prospectively evaluated CVD risk factors in a large, contemporary cohort of adults with type 1 diabetes living in the United States.</p> <p>Design</p> <p>Observational study of CVD and CVD risk factors over a median of 5.3 years.</p> <p>Setting</p> <p>The T1D Exchange clinic network.</p> <p>Patients</p> <p>Adults (age ≥18 years) with type 1 diabetes and without known CVD diagnosed before or at enrollment.</p> <p>Main Outcome Measure</p> <p>Associations between CVD risk factors and incident CVD were assessed by multivariable logistic regression.</p> <p>Results</p> <p>The study included 8,727 participants (53% female, 88% non-Hispanic white, median age 33 years [IQR=21, 48], type 1 diabetes duration 16 years [IQR=9, 26]). At enrollment, median HbA1c was 7.6% (66 mmol/mol) [IQR=6.9 (52), 8.6 (70)], 33% used a statin, and 37% used blood pressure medication. Over a mean follow-up of 4.6 years, 325 (3.7%) participants developed incident CVD. Ischemic heart disease was the most common CVD event. Increasing age, BMI, HbA1c, presence of hypertension and dyslipidemia, increasing duration of diabetes, and diabetic nephropathy were associated with increased risk for CVD. There were no significant gender differences in CVD risk.</p> <p>Conclusion</p> <p>HbA1c, hypertension, dyslipidemia and diabetic nephropathy are important risk factors for CVD in adults with type 1 diabetes. A longer follow-up is likely required to assess the impact of other traditional CVD risk factors on incident CVD in the current era.</p>
In silico database of ~500 000 virtual patients with diverse cardiovascular disease
<p>The presented dataset contains a large virtual cohort of more than 50'000 heart failure patients characterized by realistic traces of volumes, pressures, flows, and regional mechanics. We used the well-established CircAdapt model <a href="https://www.circadapt.org/">(https://www.circadapt.org/</a> , <a href="http://framework.circadapt.org/">http://framework.circadapt.org/</a>) of the human heart and circulation to simulate a large cohort of virtual patients, covering a wide range of HF-related disease heterogeneity and severity. <br>In the construction of virtual patients, generating a variety of parameter sets for the initial population is essential and can be accomplished using various sampling techniques. In our investigation, we utilized the Sobol-low discrepancy sequence to ensure uniformity across the high-dimensional parameter space. For a more detailed explanation please refer to the document <em>"MARCIUS_deliverable_D1.5_VirtualDatabase.pdf"</em></p> <p><strong>Acknowledgments:</strong> This work was supported by the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement No. 86074, <strong>"MARCIUS - MARie Curie Intelligent UltraSound"(<a href="https://www.marcius-project.com/">https://www.marcius-project.com/</a>)</strong>. MARCIUS rationale was to develop a comprehensive in silico simulation platform comprising both the generation of virtual patients (<em>presented dataset</em>) and their associated realistic image data. Such approach would make it possible to learn the most relevant patterns within a wide representative set of patients to lead the training of ML-based image processing algorithms in order to analyze real-world clinical data.</p> <p> </p>
Quality and Utility of European Cardiovascular and Orthopaedic Registries for the Regulatory Evaluation of Medical Device Safety and Performance Across the Implant Lifecycle: A Systematic Review - Dataset
<p><strong>Background: </strong>The European Union Medical Device Regulation (MDR) requires manufacturers to undertake post-market clinical follow-up (PMCF) to assess the safety and performance of their devices following approval and Conformité Européenne (CE) marking. The quality and reliability of device registries for this Regulation have not been reported. As part of the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we identified and reviewed European cardiovascular and orthopaedic registries to assess their structures, methods, and suitability as data sources for regulatory purposes.</p> <p><strong>Methods: </strong>Regional, national and multi-country European cardiovascular (coronary stents and valve repair/replacement) and orthopaedic (hip/knee prostheses) registries were identified using a systematic literature search. Annual reports, peer-reviewed publications, and websites were reviewed to extract publicly available information for 33 items related to structure and methodology in six domains and also for reported outcomes.</p> <p><strong>Results: </strong>Of the 20 cardiovascular and 26 orthopaedic registries fulfilling eligibility criteria, a median of 33% (IQR: 14%-71%) items for cardiovascular and 60% (IQR: 28%-100%) items for orthopaedic registries were reported, with large variation across domains. For instance, no cardiovascular and 16 (62%) orthopaedic registries reported patient/ procedure-level completeness. No cardiovascular and 5 (19%) orthopaedic registries reported outlier performances of devices, but each with a different outlier definition. There was large heterogeneity in reporting on items, outcomes, definitions of outcomes, and follow-up durations.</p> <p><strong>Conclusion: </strong>European cardiovascular and orthopaedic device registries could improve their potential as data sources for regulatory purposes by reaching consensus on standardised reporting of structural and methodological characteristics to judge the quality of the evidence as well as outcomes.</p>
Early Detection Of Cardiovascular Risk Factors And Definition Of Psychosocial Profile In Women Through A Systematic Approach: The Monzino Women Heart Center's Experience
<p>The raw dataset is related to the analysis of CV risk factors and psychosocial profile in women enrolled in a CV prevention program </p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
Kidney transplantation waiting times and risk of cardiovascular events and mortality: a retrospective observational cohort study in Taiwan
<p>Objectives: Patients with end-stage renal disease (ESRD) are at a high risk of cardiovascular events (CVEs), and kidney transplantation (KT) has been reported to improve risk of CVEs and survival. As the association of KT timing on long-term survival and clinical outcomes remains unclear, we investigated the association of different KT waiting times on clinical outcomes.</p> <p>Design: Retrospective observational cohort study.</p> <p>Setting: We conducted an observational cohort study using data from the National Health Insurance Research Database in Taiwan. Adult patients who initiated kidney transplantation therapy from 1997 to 2013 were included.</p> <p>Participants: A total of 3562 adult patients who initiated uncomplicated KT therapy were included and categorized into four groups according to KT waiting times after ESRD: Group 1 (<1 year), Group 2 (1–3 years), Group 3 (3–6 years), and Group 4 (>6 years).</p> <p>Primary outcome measure: The main outcome was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke, based on the primary diagnosis in medical records during hospitalization.</p> <p>Results: Compared with Group 1, the adjusted risk of primary outcome events (all-cause death, nonfatal myocardial infarction, or nonfatal stroke) increased by 1.67 times in Group 2 (95% CI: 1.40–2.00; P <0.001), 2.17 times in Group 3 (95% CI: 1.73–2.71; P <0.001), and 3.10 times in Group 4 (95% CI: 2.21–4.35; P <0.001). The rates of primary outcome events were 6.7%, 13.4%, and 14.0% within five years, increasing to 19.5%, 26.3%, and 30.8% within 10 years in Groups 1, 2, and 3, respectively.</p> <p>Conclusions: Our results demonstrate that early KT is associated with superior long-term cardiovascular outcomes compared to late KT in selected ESRD patients receiving uncomplicated KT, suggesting that an early KT could be a better treatment option for ESRD patients who are eligible for transplantation.</p>
Data set from "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data"
<p>This data set contains the complete digitalized individual patient data that are used in the manuscript "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data", which is accepted from "Acta Diabetologica".<br> The data file is in CSV format and contains the five variables "OutcomeType" (with values "AllcauseMortality" or "PrimaryOutcome"), "Study" (denoting the respective cardiovascular outcome trial), "Treatment" (denoting the respective study treatment or placebo), "Event" (denoting if the respective has been observed (Event=1) or not (Event=0)), and "SurvivalTimeMonths" (denoting the respective time to the event or censoring in months).</p>
The MicroRESUS study: An observational study to examine the effects of circulatory shock and resuscitation on microcirculatory function and mitochondrial respiration after cardiovascular surgery.
<p>Post-cardiotomy shock (PCS) occurs in up to 5% of cardiovascular surgeries and has an in-hospital mortality rate as high as 75%. Physiologic derangements are often multifactorial, including ischemic-reperfusion injury leading to low cardiac output, vasoplegia, hemorrhage, or pericardial tamponade. Resuscitation strategies often target the normalization of systemic hemodynamics, however, practices to achieve standard macrocirculatory goals often fail to reverse microcirculation abnormalities or address cytopathic hypoxia. Perioperative shock may be the result of an imbalance between oxygen delivery and oxygen demand or could be the result of impaired oxygen utilization. This study will examine differences in microcirculatory function and mitochondrial respiration in patients with circulatory shock after cardiovascular surgery.</p>
Metabolic syndrome severity score and associated cardiovascular risk in postmenopausal women of Bangladesh
<p>This data help to construct metabolic syndrome severity score and seek its association with absolute cardiovascular risk.</p>
Cardiovascular MRI (CMR) 3D Cine, 4D Flow and Exercise Stress 4D flow datasets
<p>MRI Datasets for "<strong>Motion-robust free-running volumetric cardiovascular MRI" </strong>(<a href="https://doi.org/10.1002/mrm.30123">https://doi.org/10.1002/mrm.30123</a>)</p> <p><strong>Reconstruction Code Repository</strong>: <a href="https://github.com/OSU-MR/motion-robust-CMR">https://github.com/OSU-MR/motion-robust-CMR</a><br><br><strong>Cite as</strong>: Arshad SM, Potter LC, Chen C, et al. Motion-robust free-running volumetric cardiovascular MRI. Magn Reson Med. 2024; 92(3): 1248-1262. doi: 10.1002/mrm.30123<br> </p> <p>Contains: </p> <ol> <li>3D cine Cartesian bSSFP Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>Stress 4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> </ol> <p><br><strong>Prepared by</strong>:</p> <p><strong>Syed Murtaza Arshad</strong> (<a href="arshad.32@osu.edu">arshad.32@osu.edu</a>)</p> <p>(<a href="https://github.com/syedmurtazaarshad">https://github.com/syedmurtazaarshad</a>)</p> <p> </p>
Assessment of cardiovascular risk in post-menopausal women in Ghana
<p><strong>Background</strong>: Cardiovascular diseases (CVD) continue to be major cause of death among post-menopausal women. <strong>Aim:</strong> we sought to assess cardiovascular risk among pre and post-menopausal women living within the Cape Coast Municipality, by comparing the lipid profiles and other emerging biomarkers of CVD i.e. atherogenic index of plasma (AIP), visceral adiposity index (VIA), body adiposity index (BAI) and Castelli index I (CRI-I).</p> <p><strong>Method:</strong> A cross section of 150 women (75 pre-menopausal women and 75 post-menopausal women) were randomly recruited into the study. Socio-demographic and clinical characteristics of participants were obtained with the aid of a structured questionnaire. Blood pressure (BP) was measured and Lipid profile was estimated using fasting blood samples. Other markers of cardiovascular risk such as BMI, AIP, VAI, BAI and CRI-I were estimated.</p> <p><strong>Results: </strong>We report elevated levels of total cholesterol (TC) (p<0.0001), low density lipoprotein (LDL) (p<0.0001), very low-density lipoprotein (VLDL)(p=<strong>0.0021)</strong>, Triglycerides (TG) (p<0.0001) and non-high-density lipoprotein (non-HDL-C) cholesterol (p<0.0001) in post-menopausal women in contrast to pre-menopausal women. High-density lipoprotein (HDL) (p<0.0001) was however decreased in post-menopausal women. Mean AIP (p<strong>< 0.0001)</strong>, VAI (p<strong>< 0.0001)</strong>, BAI (p<<strong> 0.0038)</strong> and CRI-I (p<<strong>0.0001)</strong> were significantly increased in post-menopausal women compared to pre-menopausal women. We also report a positive correlation of TC, TG, VLDL and NON-HDL with atherogenic markers AIP, VAI and CRI-I in post-menopausal women. A negative correlation of HDL with AIP, VAI, and CR in post-menopausal women was also observed.</p> <p><strong>Conclusion:</strong> Menopause could lead to changes in lipid profile to atherogenicity with associated increase in the risk of CVD. Atherogenic markers such as AIP, VAI, BAI, and CR can serve as potential biomarkers for predicting CVD.</p>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.