Spontaneous Coronary Artery Dissection (SCAD) refers to the spontaneous separation of the layers of the vessel wall caused by intramural hemorrhage, with or without an intimal tear. The "typical" SCAD patient is a middle-aged woman with few traditional cardiovascular risk factors, and it's frequently associated with pregnancy. Because of its low incidence, its pathophysiology is not fully understood. SCAD presents as an acute coronary syndrome, with chest pain, dyspnea, syncope, or heartbeat, even if diagnosis and clinical handling are different: coronary angiography is currently the main tool to diagnose SCAD; however, in doubtful cases, the use of both invasive and noninvasive cardiovascular imaging methods such as intravascular ultrasound or optical coherence tomography may be necessary. This paper aims to review the current state of knowledge on SCAD to address its demographic features, clinical characteristics, management, and outcomes, focusing on diagnostic algorithms and main multimodality imaging techniques.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821637 | PMC |
| http://dx.doi.org/10.3390/jcm12010154 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Imaging and management of complications post biliary-enteric anastomosis.
Abdom Radiol (NY)
January 2025
University of Virginia, Charlottesville, USA.
Biliary-enteric anastomosis is a common surgical procedure for benign and malignant pathologies involving bile ducts, pancreas and duodenum, as well as during liver transplantation. Imaging is key in detecting potential complications. Ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear scintigraphy provide complementary information.
View Article and Find Full Text PDFInfants' resting-state functional connectivity and event-related potentials: A multimodal approach to investigating the neural basis of infant novelty detection.
Dev Psychol
January 2025
Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine.
Individual differences in how the brain responds to novelty are present from infancy. A common method of studying novelty processing is through event-related potentials (ERPs). While ERPs possess millisecond precision, spatial resolution remains poor, especially in infancy.
View Article and Find Full Text PDFA Case of Bronchopneumonia Presenting With Persistent Hiccups Successfully Treated With Clonazepam and a Traditional Kampo Medicine.
Cureus
December 2024
Division of Nephrology, Toho University Sakura Medical Center, Sakura, JPN.
Persistent hiccups are rare but can serve as an early symptom of underlying conditions, including pulmonary infections and cerebrovascular disorders. This case highlights hiccups as a presenting symptom of bronchopneumonia in a hemodialysis patient and explores the effective use of chlorpromazine and Hange-koboku-to (HKT) as symptomatic therapies. Given the potential association of hiccups with neurological conditions, this case underscores the need for comprehensive diagnostic evaluation.
View Article and Find Full Text PDFDesign of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.
Heliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFMultimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.
Front Physiol
December 2024
Department of Radiology, Yiyang Central Hospital, Yiyang, China.
Objectives: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.
Methods: This retrospective study collected data from 360 patients with uterine fibroids who underwent HIFU treatment. The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!