3 resultados para Pulse Measurement
Resumo:
Background: Approximately 5% of the population donates blood each year in developed countries. Recruiting and maintaining a pool of altruistic and healthy blood donors is a challenging task. Blood donation as a dynamic process must naturally co-exist with the arguably essential deferrals. Aims: To analyse a 11-year cohort of donors and blood donations in order to determine the profile of the average donor and the typical donation. Characterize the donor’s population in terms of gender, age, number of donations, most common causes for deferral and exclusion and the possible relationships between them. Establish the tendency flow of donations per year. Methods: Analysis of 95861 blood donations from 31550 donors collected between 2000 and 2010 (11 years) in the Immunohemotherapy Department of the ‘‘Centro Hospitalar Lisboa Central - Hospital de Sa˜o Jose´’’ (Lisboa, Portugal). Prior to blood donation, donors were required to fill out a form of informed consent, a questionnaire of 21 ‘‘yes or no’’ questions and were submitted to a clinical assessment and physical examination including: measurement of weight, blood pressure, pulse and capillary hemoglobin levels. Post-donation, the collected blood was tested for ALT elevation and blood-borne viral agents (HBV, HCV, HIV 1 and 2 and HTLV 1 and 2) and other infections (Treponema pallidum). Blood donors and donations were registered in a database and statistically studied in terms of: gender and age distribution, number of donations, most common causes for deferral and exclusion. The frequency of blood donations throughout the period of observation was analyzed and statistically significant relationships between the collected variables were investigated. Results: From the population of 31550 donors 61% were male and a mean age of 41.5 years (± 12.5 years) was found. From the total of 95682 blood donations collected 78% were successful while the most common causes for deferral were: donation incompatible hemoglobin levels (5% of the blood donations and 22% of deferrals), ALT elevation (3% and 14%), positive blood screening test for Treponema pallidum (1% and 6%), medication (1% and 4%), positive serological blood markers for HBV (1% and 4%), endoscopy in the previous 12 months (1% and 3%), arterial hypertension (1% and 3%), infectious conditions (1% and 3%), influenza or influenza-like symptoms (1% and 2%) and positive serological blood markers for HCV (1% and 2%). Summary/Conclusions: Several factors may have contributed to a limited number of new regular donors in the population, namely: ageing population, the alienation of the individual from the community induced by modern lifestyles and job precariousness. It is of the utmost importance to refine our blood donation campaigns according to the existing population of donors. The optimization of the blood donation potential of a population of donors must be achieved through the development of reliable and consistent screening methods. In order to appeal to new donors it is important to promote blood donations considering the profile of the regular and healthy blood donor of the existing population.
Resumo:
PURPOSE: To determine the correlation between ocular blood flow velocities and ocular pulse amplitude (OPA) in glaucoma patients using colour Doppler imaging (CDI) waveform analysis. METHOD: A prospective, observer-masked, case-control study was performed. OPA and blood flow variables from central retinal artery and vein (CRA, CRV), nasal and temporal short posterior ciliary arteries (NPCA, TPCA) and ophthalmic artery (OA) were obtained through dynamic contour tonometry and CDI, respectively. Univariate and multiple regression analyses were performed to explore the correlations between OPA and retrobulbar CDI waveform and systemic cardiovascular parameters (blood pressure, blood pressure amplitude, mean ocular perfusion pressure and peripheral pulse). RESULTS: One hundred and ninety-two patients were included [healthy controls: 55; primary open-angle glaucoma (POAG): 74; normal-tension glaucoma (NTG): 63]. OPA was statistically different between groups (Healthy: 3.17 ± 1.2 mmHg; NTG: 2.58 ± 1.2 mmHg; POAG: 2.60 ± 1.1 mmHg; p < 0.01), but not between the glaucoma groups (p = 0.60). Multiple regression models to explain OPA variance were made for each cohort (healthy: p < 0.001, r = 0.605; NTG: p = 0.003, r = 0.372; POAG: p < 0.001, r = 0.412). OPA was independently associated with retrobulbar CDI parameters in the healthy subjects and POAG patients (healthy CRV resistance index: β = 3.37, CI: 0.16-6.59; healthy NPCA mean systolic/diastolic velocity ratio: β = 1.34, CI: 0.52-2.15; POAG TPCA mean systolic velocity: β = 0.14, CI 0.05-0.23). OPA in the NTG group was associated with diastolic blood pressure and pulse rate (β = -0.04, CI: -0.06 to -0.01; β = -0.04, CI: -0.06 to -0.001, respectively). CONCLUSIONS: Vascular-related models provide a better explanation to OPA variance in healthy individuals than in glaucoma patients. The variables that influence OPA seem to be different in healthy, POAG and NTG patients.