4 resultados para 316

em Université de Lausanne, Switzerland


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Background: Leptin is produced primarily by adipocytes. Although originally associated with the central regulation of satiety and energy metabolism, increasing evidence indicates that leptin may be an important factor for congestive heart faire (CHF). In the study, we aimed to test the hypothesis that leptin may influence CHF pathophysiology via a pathway of increasing body mass index (BMI). Methods: We studied 2,389 elderly participants aged 70 and older (M; 1161, F: 1228) without CHF and with serum leptin measures at the Health Aging, and Body Composition study. We analyzed the association between serum leptin level and risk of incident CHF using Cox hazard proportional regression models. Elevated leptin level was defined as more than the highest quartile (Q4) of leptin distribution in the total sample for each gender. Adjusted-covariates included demographic, behavior, lipid and inflammation variables (partially-adjusted models), and further included BMI (fully-adjusted models). Results: In a mean 9-year follow-up, 316 participants (13.2%) developed CHF. The partially-adjusted models indicated that men and women with elevated serum leptin levels (>=9.89 ng/ml in men and >=25 ng/ml in women) had significantly higher risks of developing CHF than those with leptin level of less than Q4. The adjusted hazard ratios (95%CI) for incident CHF was 1.49 (1.04 -2.13) in men and 1.71 (1.12 -2.58) in women. However, these associations became non-significant after adjustment for including BMI for each gender. The fully-adjusted hazard ratios (95%CI) were 1.43 (0.94 -2.18) in men and 1.24 (0.77-1.99) in women. Conclusion: Subjects with elevated leptin levels have a higher risk of CHF. The study supports the hypothesis that the influence of leptin level on risk of CHF may be through a pathway related to increasing BMI.

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BACKGROUND: To date, there is no quality assurance program that correlates patient outcome to perfusion service provided during cardiopulmonary bypass (CPB). A score was devised, incorporating objective parameters that would reflect the likelihood to influence patient outcome. The purpose was to create a new method for evaluating the quality of care the perfusionist provides during CPB procedures and to deduce whether it predicts patient morbidity and mortality. METHODS: We analysed 295 consecutive elective patients. We chose 10 parameters: fluid balance, blood transfused, Hct, ACT, PaO2, PaCO2, pH, BE, potassium and CPB time. Distribution analysis was performed using the Shapiro-Wilcoxon test. This made up the PerfSCORE and we tried to find a correlation to mortality rate, patient stay in the ICU and length of mechanical ventilation. Univariate analysis (UA) using linear regression was established for each parameter. Statistical significance was established when p < 0.05. Multivariate analysis (MA) was performed with the same parameters. RESULTS: The mean age was 63.8 +/- 12.6 years with 70% males. There were 180 CABG, 88 valves, and 27 combined CABG/valve procedures. The PerfSCORE of 6.6 +/- 2.4 (0-20), mortality of 2.7% (8/295), CPB time 100 +/- 41 min (19-313), ICU stay 52 +/- 62 hrs (7-564) and mechanical ventilation of 10.5 +/- 14.8 hrs (0-564) was calculated. CPB time, fluid balance, PaO2, PerfSCORE and blood transfused were significantly correlated to mortality (UA, p < 0.05). Also, CPB time, blood transfused and PaO2 were parameters predicting mortality (MA, p < 0.01). Only pH was significantly correlated for predicting ICU stay (UA). Ultrafiltration (UF) and CPB time were significantly correlated (UA, p < 0.01) while UF (p < 0.05) was the only parameter predicting mechanical ventilation duration (MA). CONCLUSIONS: CPB time, blood transfused and PaO2 are independent risk factors of mortality. Fluid balance, blood transfusion, PaO2, PerfSCORE and CPB time are independent parameters for predicting morbidity. PerfSCORE is a quality of perfusion measure that objectively quantifies perfusion performance.