Background Delirium is an acute and reversible geriatric syndrome that represents a decompensation of cerebral function. Delirium is associated with adverse postoperative outcomes, but controversy exists regarding whether delirium is an independent predictor of mortality. Thus, we assessed the association between incident postoperative delirium and mortality in adult noncardiac surgery patients. Methods A systematic search was conducted using Cochrane, MEDLINE/PubMed, Cumulative Index to Nursing and Allied Health Literature, and Embase. Screening and data extraction were conducted by two independent reviewers. Pooled-effect estimates calculated with a random-effects model were expressed as odds ratios with 95% CIs. Risk of bias was assessed using the Cochrane Risk of Bias Tool for Non-Randomized Studies. Results A total of 34 of 4,968 screened citations met inclusion criteria. Risk of bias ranged from moderate to critical. Pooled analysis of unadjusted event rates (5,545 patients) suggested that delirium was associated with a four-fold increase in the odds of death (odds ratio = 4.12 [95% CI, 3.29 to 5.17]; I2 = 24.9%). A formal pooled analysis of adjusted outcomes was not possible due to heterogeneity of effect measures reported. However, in studies that controlled for prespecified confounders, none found a statistically significant association between incident postoperative delirium and mortality (two studies in hip fractures; n = 729) after an average follow-up of 21 months. Overall, as study risk of bias decreased, the association between delirium and mortality decreased. Conclusions Few high-quality studies are available to estimate the impact of incident postoperative delirium on mortality. Studies that controlled for prespecified confounders did not demonstrate significant independent associations of delirium with mortality.
Short‐term consumption of a high‐fat diet (HFD) can result in an oxidative shift in adult skeletal muscle. However, the impact of HFD on young, growing muscle is largely unknown. Thus, 4‐week‐old mice were randomly divided into sedentary HFD (60% kcal from fat), sedentary standard chow (control), or exercise‐trained standard chow. Tibialis anterior (TA) and soleus muscles were examined for morphological and functional changes after 3 weeks. HFD consumption increased body and epididymal fat mass and induced whole body glucose intolerance versus control mice. Compared to controls, both HFD and exercise‐trained TA muscles displayed a greater proportion of oxidative fibers and a trend for an increased succinate dehydrogenase (SDH) content. The soleus also displayed an oxidative shift with increased SDH content in HFD mice. Despite the aforementioned changes, palmitate oxidation rates were not different between groups. To determine if the adaptive changes with HFD manifest as a functional improvement, all groups performed pre‐ and postexperiment aerobic exercise tests. As expected, exercise‐trained mice improved significantly compared to controls, however, no improvement was observed in HFD mice. Interestingly, capillary density was lower in HFD muscles; a finding which may contribute to the lack of functional differences seen with HFD despite the oxidative shift in skeletal muscle morphology. Taken together, our data demonstrate that young, growing muscle exhibits early oxidative shifts in response to a HFD, but these changes do not translate to functional benefits in palmitate oxidation, muscle fatigue resistance, or whole body exercise capacity.
The term "vasculogenic mimicry" (VM) refers to the phenomenon in which vascular-like channels, which are not lined by endothelial cells, are formed in tumors. Since its discovery in 1999, it has been observed in several tumor types and is proposed to provide blood perfusion to tumors in absence of co-apted or neo-angiogenic blood vessels. Pituitary tumors are generally slow growing, benign adenomas which are less vascularized than the normal pituitary gland. To date, VM in pituitary adenomas has not been described. In this histological study, we assessed the presence of VM in a series of surgically resected clinically non-functioning pituitary adenomas (NFPAs) using CD34 and Periodic Acid-Schiff (PAS) double staining. To identify VM, slides were assessed for the presence of CD34-negative and PAS-positive channels indicating that they were not lined by endothelial cells. The histological staining pattern suggestive of VM was noted in 22/49 (44.9%) of the specimens studied. VM was observed in both recurring and non-recurring NFPAs. The incidence of VM present varied from case to case and within groups. There was no association between the presence of VM and gender, tumor size, Ki-67 index, recurrence or cavernous sinus invasion. VM was not noted in cases of non-tumorous pituitaries. Our findings suggest the existence of a complementary perfusion system in pituitary adenomas, implying potential clinical implications with respect to response to therapy and clinical course. Further research is warranted to confirm the presence of VM in pituitary adenomas to elucidate its clinical relevance in patients diagnosed with a pituitary adenoma.
ImportanceArtificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.ObjectiveTo evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.Data SourcesA systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.Study SelectionStudies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.Main Outcomes and MeasuresDiagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.ResultsOf 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).Conclusions and RelevanceThe findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.
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