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1.

Objectives

Expedient extubation after cardiac surgery has been associated with improved outcomes, leading to postoperative extubation frequently during overnight hours. However, recent evidence in a mixed medical-surgical intensive care unit population demonstrated worse outcomes with overnight extubation. This study investigated the impact of overnight extubation in a statewide, multicenter Society of Thoracic Surgeons database.

Methods

Records from 39,812 patients undergoing coronary artery bypass grafting or valve operations (2008-2016) and extubated within 24 hours were stratified according to extubation time between 06:00 and 18:00 (day) or between 18:00 and 6:00 (overnight). Outcomes including reintubation, mortality, and composite morbidity-mortality were evaluated using hierarchical regression models adjusted for Society of Thoracic Surgeons predictive risk scores. To further analyze extubation during the night, a subanalysis stratified patients into 3 groups: 06:00 to 18:00, 18:00 to 24:00, and 24:00 to 06:00.

Results

A total of 20,758 patients were extubated overnight (52.1%) and were slightly older (median age 66 vs 65 years, P < .001) with a longer duration of ventilation (4 vs 7 hours, P < .001). Day and overnight extubation were associated with equivalent operative mortality (1.7% vs 1.7%, P = .880), reintubation (3.7% vs 3.4%, P = .141), and composite morbidity-mortality (8.2% vs 8.0%, P = .314). After risk adjustment, overnight extubation was not associated with any difference in reintubation, mortality, or composite morbidity-mortality. On subanalysis, those extubated between 24:00 and 06:00 exhibited increased composite morbidity-mortality (odds ratio, 1.18; P = .001) but no difference in reintubation or mortality.

Conclusions

Extubation overnight was not associated with increased mortality or reintubation. These results suggest that in the appropriate clinical setting, it is safe to routinely extubate cardiac surgery patients overnight.  相似文献   
2.
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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Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.  相似文献   
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BACKGROUND

Online physician rating websites are increasingly used by patients to evaluate their doctors. The purpose of this investigation was to evaluate factors associated with better spine surgeon ratings.

METHODS

Orthopedic spine surgeons were randomly selected from the North American Spine Society directory utilizing a random number generator. Surgeon profiles on three physician rating websites, namely, www.HealthGrades.com, www.Vitals.com, and www.RateMDs.com, were analyzed to gather qualitative and quantitative data on patients’ perceptions of the surgeons. Independent variables from the websites were analyzed in relation to overall physician or patient satisfaction rating. Comments were coded by subject into following three categories: professional competence, bedside manner, and practice characteristics.

RESULTS

A total of 250 surgeons were evaluated, and 92% (n=230) of these doctors had at least one rating among the three websites. The surgeons with a higher average rating had significantly better trust (p<.01), scheduling (p<.01), staff (p<.01), helpfulness (p<.01), and punctuality (p<.01) scores but significantly less experience (p<.05). A linear regression model for the average rating of each surgeon (R2 value=0.754) yielded only following three significant variables: trustworthiness (p<.01), experience match (p<.05), and the average number of negative comments on surgeon's professional competence (p<.05). Trustworthiness (β=0.749) was the strongest predictor variable of physician rating, followed by the number of negative professional competence comments (β=?0.132) and experience match (β=?0.112).

CONCLUSIONS

This investigation assessed spine surgeon online patient ratings and categorized factors that patients associate with quality care. Trustworthiness was the most significant predictor of positive ratings, whereas ease of scheduling, quality of staff, helpfulness, and punctuality were also associated with higher patient ratings. Understanding what patients value may help optimize care of spine surgery patients.  相似文献   
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