Objective: This study aimed to identify the determinants affecting the time required for blood pressure (BP) restoration after autonomic dysreflexia (AD) and to propose a new method for BP measurement in individuals with cervical spinal cord injury (SCI) who experience AD.
Methods: In a prospective, single-center observational study, participants' bladders were filled with body-temperature saline until reaching cystometric capacity, as confirmed by previous urodynamic studies. Restoration time (RT), defined as the time from the onset of voiding until BP returned to baseline, was measured during the morning voiding session. This session involved the use of a 10F hydrophilic transurethral catheter. Data were then compared with various clinical determinants including demographic, urodynamic, and cystographic variables.
Results: The study included 29 individuals with cervical SCI. Notable variations in RT were observed among individuals with differing levels of detrusor overactivity (DO) and bladder compliance. An inverse correlation was noted between RT and bladder compliance, whereas positive correlations were identified with maximal detrusor pressure, peak systolic BP (SBP), and the magnitude of BP changes. Factors associated with prolonged RT included injury completeness, bladder trabeculation, vesicoureteral reflux (VUR), DO, and changes in SBP.
Conclusions: A significant association was found between BP elevation and prolonged RT. Determinants such as the severity of the SCI, bladder trabeculation, VUR, and DO were correlated with prolonged RT, considering their importance in the assessment of baseline BP following AD.
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http://dx.doi.org/10.1080/10790268.2024.2335693 | DOI Listing |
Circ Genom Precis Med
January 2025
Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Los Angeles. (W.F., N.D.W.).
Background: Lp(a; Lipoprotein[a]) is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).
Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies.
Hypertension
January 2025
Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany (S.A.P., I.Q., D. Arifaj, M.K., D. Argov, L.C.R., J.S.).
Background: Ciliary neurotrophic factor (CNTF), mainly known for its neuroprotective properties, belongs to the IL-6 (interleukin-6) cytokine family. In contrast to IL-6, the effects of CNTF on the vasculature have not been explored. Here, we examined the role of CNTF in AngII (angiotensin II)-induced hypertension.
View Article and Find Full Text PDFWounds from gunshots and other explosive devices are a source of loss of substances directly or secondary to a well- conducted debridement. In addition, these types of wounds are by definition contaminated. The major challenge in this context for any surgeon remains coverage.
View Article and Find Full Text PDFJHEP Rep
February 2025
Department of Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Instituto Ramon y Cajal de Investigación Sanitaria (IRYCIS), Universidad de Alcalá, Madrid, Spain.
Background & Aims: Systemic inflammation is a driver of decompensation in cirrhosis with unclear relevance in the compensated stage. We evaluated inflammation and bacterial translocation markers in compensated cirrhosis and their dynamics in relation to the first decompensation.
Methods: This study is nested within the PREDESCI trial, which investigated non-selective beta-blockers for preventing decompensation in compensated cirrhosis and clinically significant portal hypertension (CSPH: hepatic venous pressure gradient ≥10 mmHg).
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
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