The prognostic value of sleep blood pressure reported by recent studies is variable. Our aim was to examine the relationship of sleep blood pressure, measured by 24-hour ambulatory blood pressure monitoring, with all-cause mortality. We studied a cohort of 3957 patients aged 55+/-16 (58% treated) referred for ambulatory monitoring (1991-2005). Sleep, including daytime sleep, was recorded by diary. Linkage with the national population register identified 303 deaths during 27 750 person-years of follow-up. Hazard ratios (HRs) for mortality in Cox proportional hazards models that included age, sex, hypertension, and diabetes treatment were 1.32 (95% CI: 0.99 to 1.76) for awake hypertension (>or=135/85 mm Hg), and 1.67 (95% CI: 1.25 to 2.23) for sleep hypertension (>or=120/70 mm Hg). By quintile analysis, the upper fifths of systolic and diastolic dipping during sleep were associated with adjusted HRs of 0.58 (95% CI: 0.41 to 0.82) and 0.68 (95% CI: 0.48 to 0.96), respectively. In a model controlling for awake systolic blood pressure, hazards associated with reduced systolic dipping increased from dippers (>10%; HR: 1.0), through nondippers (0% to 9.9%; HR: 1.30; 95% CI: 1.00 to 1.69) to risers (<0%; HR: 1.96; 95% CI: 1.43 to 2.96). Thus, in practice, ambulatory blood pressure predicts mortality significantly better than clinic blood pressure. The availability of blood pressure measures during sleep and, in particular, the pattern of dipping add clinically predictive information and provide further justification for the use of ambulatory monitoring in patient management.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1161/HYPERTENSIONAHA.107.087262 | 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).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!