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Oncology Provider and Patient Perspectives on a Cardiovascular Health Assessment Tool utilized during Post-treatment Survivorship Care in Community Oncology (Results from WF-1804CD): Mixed Methods Observational Study.
J Med Internet Res
January 2025
Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, US.
Background: Most cancer survivors have multiple cardiovascular risk factors, increasing their risk of poor cardiovascular and cancer outcomes. The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association's Life's Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors.
View Article and Find Full Text PDFDeep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting.
Bioengineering (Basel)
January 2025
Section of Cardiac Surgery, Department of Surgery, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, USA.
Spheroids serve as the building blocks for three-dimensional (3D) bioprinted tissue patches. When larger than 500 μm, the desired size for 3D bioprinting, they tend to have a hypoxic core with necrotic cells. Therefore, it is critical to assess the viability of spheroids in order to ensure the successful fabrication of high-viability patches.
View Article and Find Full Text PDFFeasibility analysis of non-electrocardiogram-triggered chest low-dose computed tomography using a kV-independent reconstruction algorithm for predicting cardiovascular disease risk in patients receiving maintenance hemodialysis.
BMC Cardiovasc Disord
January 2025
Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Objectives: This study aimed to evaluate the feasibility and accuracy of non-electrocardiogram (ECG)-triggered chest low-dose computed tomography (LDCT) with a kV-independent reconstruction algorithm in assessing coronary artery calcification (CAC) degree and cardiovascular disease risk in patients receiving maintenance hemodialysis (MHD).
Methods: In total, 181 patients receiving MHD who needed chest CT and coronary artery calcium score (CACS) scannings sequentially underwent non-ECG-triggered, automated tube voltage selection, high-pitch chest LDCT with a kV-independent reconstruction algorithm and ECG-triggered standard CACS scannings. Then, the image quality, radiation doses, Agatston scores (ASs), and cardiac risk classifications of the two scans were compared.
Heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) are increasingly prevalent cardiovascular conditions, particularly among the elderly population. These two conditions share common risk factors and often coexist, leading to a complex interplay that alters the clinical course of each other. The pathophysiology of HFpEF is multifaceted and intricately linked, with atrial disease serving as a common pathophysiological pathway.
View Article and Find Full Text PDFPatient reported outcome measures in cardiovascular research and care: PRO(M)s and CONS.
J Cardiovasc Pharmacol
January 2025
Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy.
Patient-reported outcome measures (PROMs) are vital tools in cardiovascular disease (CVD) research and care, providing insights that complement traditional clinical outcomes like mortality and morbidity. PROMs capture patient experiences with CVD, such as quality of life, functional capacity, and emotional well-being, allowing clinicians to assess how interventions impact daily life. PROMs are integral to cardiovascular investigations as well as management, especially in chronic conditions and rehabilitation, where they inform on the impact of personalized care plans by tracking symptom progression and patient adherence.
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