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Post-induction hypotension is common and associated with postoperative complications. We hypothesised that pneumatic leg compression reduces post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy. In this double-blind randomised study, patients were allocated randomly to the pneumatic leg compression group (n = 50) or control (n = 50). In the intervention group, pneumatic leg compression was initiated before induction of anaesthesia. In the control group, pneumatic leg compression was initiated 20 min after anaesthesia induction. The primary outcome was the incidence of post-induction hypotension in these groups. Post-induction hypotension was defined as systolic blood pressure < 90 mmHg during the first 20 min after induction. Haemodynamic variables and area under the curve of post-induction systolic blood pressure over time were assessed. Complications associated with pneumatic leg compression were recorded, including: peripheral neuropathy; compartment syndrome; extensive bullae beneath the leg sleeves; and pulmonary thromboembolism. The incidence of post-induction hypotension decreased in the pneumatic leg compression group compared with that in the control group; 5 (10%) vs. 29 (58%), respectively, p < 0.001. In the pneumatic leg compression group, the lowest systolic, diastolic and mean blood pressures 20 min after induction of anaesthesia were significantly greater than the control group. Pneumatic leg compression resulted in an increased area under the curve of systolic blood pressure in the first 20 min after induction, p = 0.001. There were no pneumatic leg compression-related complications. Pneumatic leg compression reduced post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy, suggesting that it is an effective and safe intervention to prevent post-induction hypotension among elderly patients undergoing general anaesthesia.  相似文献   
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Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).  相似文献   
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