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http://dx.doi.org/10.1016/j.ijcard.2014.07.274 | DOI Listing |
BMC Neurol
January 2025
Department of Intensive Care Unit, Shijiazhuang People's Hospital, No. 09 of FangBei Road, Chang'an District, Shijiazhuang, 050000, China.
Objective: This study aims to evaluate the clinical significance of ultrasound-based measurement of optic nerve sheath diameter (ONSD) in predicting intracerebral hemorrhage (ICH) complicated by cerebral-cardiac syndrome (CCS).
Methods: Patients with ICH and who were treated in the intensive care unit (ICU) at Shijiazhuang People's Hospital between October 2021 and November 2022 were included in this study. Participants were divided into two groups: those with CCS and those without.
BMC Cardiovasc Disord
January 2025
ITACA Institute, Universitat Politècnica de València, València, Spain.
Background: Complexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics.
Methods: BSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.
Heliyon
January 2025
Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands.
Objective And Rationale: Small studies have shown that the QT interval follows a circadian rhythm. This finding has never been confirmed in a large real-world hospital population and the clinical meaning of disrupted rhythmicity remains unknown.
Methods: In this cohort study, all consecutive adult patients with at least one 12-lead ECG acquired between 1991 and 2021 were considered.
JACC Clin Electrophysiol
January 2025
Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA. Electronic address:
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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