Background: Accurate blood pressure measurement is dependent on using a blood pressure cuff that is appropriate to the patient's arm circumference.
Objective: This study identifies the change in distribution of arm circumferences in the United States and its impact on usage of the 'standard adult' cuff.
Method: The most current National Health and Nutritional Examination Survey (NHANES) data, available on the website (http://www.cdc.gov/nchswww/nchshome.htm), covers NHANES III (Cycle 1, 1988-1991; 8381 subjects and Cycle 2, 1991-1994; 8566 subjects) and NHANES 2000 (1999-2000; 4,444 subjects). The weighted change in arm circumference between NHANES III as a whole and NHANES 2000 was calculated. Correlates for increased arm circumference were obtained via multivariate analysis. The predicted change in blood pressure cuff usage based on the changing prevalence of arm circumferences was calculated.
Results: The mean arm circumference for the whole population increased significantly from NHANES III to NHANES 2000 (31.83 +/- 0.08 cm versus 32.86 +/- 0.15 cm, p < 0.001) and in the hypertension population without reaching statistical significance (33.07 +/- 0.16 to 33.61 +/- 0.30 cm, p = 0.11). When adjusted regressions were performed, the increases in arm circumference were due to increasing weight in both the whole population and the hypertensives. The numbers of Americans predicted to require the 'standard adult' cuff to accurately measure blood pressure decreased from 76.2% to 66.3% while the number of Americans increased from 42.2% to 45%.
Conclusions: The increasing prevalence of overweight and obese Americans found in NHANES III and NHANES 2000 has led to larger mean arm circumferences in hypertensives and the total population. This increased frequency of larger arm circumferences predicts that the 'large adult' cuff will be increasingly required in clinician's offices for accurate blood pressure measurement.
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http://dx.doi.org/10.1097/00126097-200312000-00001 | 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
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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.
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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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