Objective: To compare the anti-hypertensive efficacy, safety, and tolerability of irbesartan with those of the full dose range of enalapril in patients with mild-to-moderate hypertension.
Design And Methods: A total of 200 patients were randomised to irbesartan 75 mg or enalapril 10 mg (once daily). Doses were doubled at Weeks 4 and/or 8 if seated diastolic blood pressure (DBP) was > or = 90 mm Hg. Trough blood pressure was measured after completion of a 4- to 5-week placebo lead-in period and again after 2, 4, 8, and 12 weeks of treatment.
Main Outcome Measures: Efficacy was evaluated by determining the change from baseline in trough seated blood pressure and the proportion of patients normalised (seated DBP <90 mm Hg) at Week 12. Safety and tolerability were assessed by adverse events reported by physicians, by patients in response to a specific-symptoms questionnaire, by open-ended questioning of patients by physicians, and by clinical laboratory evaluations.
Results: Both treatments significantly lowered blood pressure with no significant difference in efficacy between treatment groups. At Week 12, the percentage of patients titrated to either enalapril 40 mg or irbesartan 300 mg was 24% and 28%, respectively. The frequency of overall adverse events was similar in both groups. The incidence of cough in the enalapril and irbesartan groups was 17% and 10%, respectively. In contrast to other AII receptor antagonists, there was no change in uric acid concentrations with irbesartan.
Conclusions: Irbesartan was as effective as the full dose range of enalapril. Irbesartan also demonstrated an excellent tolerability profile.
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http://dx.doi.org/10.1038/sj.jhh.1000591 | 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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