Skyttepoole8696
Evidence is limited concerning the association between serum concentrations of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-I (APO A-I) and severe acute pancreatitis (SAP). This study was designed to explore whether HDL-C and APO A-I were independently correlated to SAP after adjusting for covariates.
There were 1127 patients with acute pancreatitis who were recruited from a tertiary teaching hospital in Wenzhou from 1 January 2018 to 30 April 2020. The independent variables were baseline levels of HDL-C, and APO A-I collected within 24 h after admission. MCC950 The dependent variable was the occurrence of SAP during hospitalization. Univariate and multivariate binary logistic regression were conducted to analyze the relationship between HDL-C and APO A-I and SAP. The receiver operating characteristic curve was applied to analyze the prediction power of lipid parameters and C-reactive protein for SAP.
The incidence of SAP was 11.5% among the 678 patients included in the final analysis. The serum levels of APO A-I and HDL-C were negatively related to SAP after adjusting for confounders with an odds ratio of 0.24 [95% confidence interval (CI) 0.06-0.95] and 0.16 (95% CI, 0.04-0.56), respectively. APO A-I (area under the curve = 0.69; 95% CI, 0.63-0.76) and HDL-C (area under the curve = 0.72; 95% CI, 0.66-0.79) showed higher predictive value for SAP compared with other lipid parameters.
Decreased serum concentrations of HDL-C and APO A-I are associated with SAP after adjusting for covariates.
Decreased serum concentrations of HDL-C and APO A-I are associated with SAP after adjusting for covariates.When provided with opportunities to view the world from the patients' perspective, nursing students can experience the same practical occurrences and feelings that patients encounter, consequently becoming more aware of their discomfort and pain. This study aimed to evaluate the performance of the patient experience virtual reality blended learning program developed for nursing students. This study is significant in that it presents a program that enables nursing students to not only experience being perioperative patients themselves but also experience their conditions in places other than hospitals, which are generally used as training locations. The analytical results of this study indicated that nursing students who virtually experienced the conditions of perioperative patients through virtual reality blended learning showed increased levels of empathy, positive attitudes toward patient safety treatment, confidence in nursing care, and clinical skill performance. The developed program in this study blended various teaching methods with a virtual reality platform to help junior nursing students with practical and effective perioperative training increase their levels of empathy by simulating the experiences and perspectives of perioperative patients.Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedridden duration of hospitalized patients based on EMR at admission by machine learning. The medical data of 4345 hospitalized patients who were bedridden for at least 24 hours after admission were retrospectively collected. After preprocessing of the data, features for modeling were selected by support vector machine recursive feature elimination. Thereafter, logistic regression, support vector machine, and extreme gradient boosting algorithms were adopted to predict the bedridden duration. The feasibility and efficacy of above models were evaluated by performance indicators. Our results demonstrated that the most important features related to bedridden duration were Charlson Comorbidity Index, age, bedridden duration before admission, mobility capability, and perceptual ability. The extreme gradient boosting algorithm showed the best performance (accuracy, 0.797; area under the curve, 0.841) when compared with support vector machine (accuracy, 0.771; area under the curve, 0.803) and logistic regression (accuracy, 0.765; area under the curve, 0.809) algorithms. Meanwhile, the extreme gradient boosting algorithm had a higher sensitivity (0.856), specificity (0.650), and F1 score (0.858) than that of support vector machine algorithm (0.843, 0.589, and 0.841) and logistic regression (0.852, 0.545, and 0.839), respectively. These findings indicate that machine learning based on EMRs at admission is a feasible avenue to predict the bedridden duration. The extreme gradient boosting algorithm shows great potential for further clinical application.As part of the development and testing of an innovative technology for tracking disinfection of portable medical equipment, end-user feedback was obtained during an initial trial on two acute care hospital units. The disinfection tracking device was installed on the computers-on-wheels and vital signs machines. Each device had the capability of detecting a cleaning event, reporting the event to an online database, and displaying the time since last cleaning event on a visual display. End-user feedback regarding functionality, usefulness of information provided, and impact on workflow was obtained by survey and facilitated group discussions. Seventeen frontline nurses completed the anonymous survey, and 22 participated in the facilitated group discussions. End users found the system functionally easy to use and the information about time since last cleaning useful and reported minimum disruption of workflow. Functionality of the system was confirmed by consistency between recorded and self-reported cleaning patterns. Managers found the data on cleaning of portable medical equipment helpful in validating compliance with hospital equipment cleaning policy. Frontline staff expressed appreciation for technology that helps them and improves outcomes but also discussed concerns about the potential for technology that creates extra work and disruption in the busy frontline nursing care delivery environment. Nurses were appreciative of opportunities to provide feedback and input into efforts to develop and introduce technology. Recorded cleaning events coincided with self-reported equipment cleaning patterns and illustrated that the device efficiently collects information deemed useful by the end user.
Allergic disorders are the result of complex interactions between genetic predisposition and environmental exposures. Elucidating how specific environmental exposures contribute to allergic diseases in adults is crucial, especially as the world population ages in a rapidly changing environment.
The effects of environmental exposures on allergic diseases remain understudied in adults. Although epidemiological studies suggest various environmental exposures are associated with the development and exacerbation of allergic diseases, further longitudinal studies are needed across various age groups in adults to pinpoint the exposures of concerns and the time windows of susceptibility. Mechanistic studies in adults are few. A multicomponent strategy targeting several allergens has been conditionally recommended for asthma, but recent findings on mitigation strategies remain limited.
Further research on how environmental exposures cause and exacerbate allergic disorders is needed in adults, particularly across disease phenotypes. The effects of mitigation strategies against environmentally induced adult allergic diseases remain large research gaps. A better understanding of how and which environmental exposures contribute to allergic disorders is necessary to identify patients who are at higher risk and would benefit from specific interventions.
Further research on how environmental exposures cause and exacerbate allergic disorders is needed in adults, particularly across disease phenotypes. The effects of mitigation strategies against environmentally induced adult allergic diseases remain large research gaps. A better understanding of how and which environmental exposures contribute to allergic disorders is necessary to identify patients who are at higher risk and would benefit from specific interventions.
Rectal cancer is common in developed countries, though incidence varies globally. We assessed time trends in incidence, relative survival and mortality in Denmark.
Rectal cancer cases (N = 50 461) diagnosed in 1978-2018 were identified in the Danish Cancer Registry. We calculated age-standardized incidence rates, overall and according to sex and age. Average annual percentage changes (AAPC) were estimated using Poisson regression. link2 We estimated 5-year relative survival and evaluated the effect of age, calendar year of diagnosis, sex and stage of disease on mortality using the Cox proportional hazards model.
The incidence of rectal cancer tended to decrease in all age groups and both sexes during 1978-1997, but increased since 1998, more in men (AAPC = 2.05%; 95% CI,1.80; 2.31) than in women (AAPC = 0.99%; 95% CI,0.68; 1.30). It increased in men until 79 years and in women up to 59 years. Mortality decreased over time when adjusting for age, stage and sex. Overall, men had the highest 5-year mortality after adjusting for age, calendar period and stage. Five-year relative survival improved (1978-2018) for all stages. Initially, the overall 5-year relative survival tended to be better for women, but in recent years, it has been similar in both sexes.
Incidence of rectal cancer increased in the last two decades, most markedly in women 59 years and younger. Mortality decreased when adjusting for age and stage. Relative survival improved over time more for men than for women, so in recent years, it has been virtually identical in men and women.
Incidence of rectal cancer increased in the last two decades, most markedly in women 59 years and younger. Mortality decreased when adjusting for age and stage. Relative survival improved over time more for men than for women, so in recent years, it has been virtually identical in men and women.Aiming to detect age, period and cohort effects in cancer mortality, age-period-cohort models (APC) can be applied to distinguish these effects. The main difficulty with adjusting an APC model involving age, period and cohort factors is the linear relationship between them, leading to a condition known as the 'nonidentifiability problem'. Many methods have been developed by statisticians to solve it, but there is not a consensus. All these existing methods, with their advantages and disadvantages, create confusion when choosing which one of them should be implemented. In this context, the present scoping review intends not to show all methods developed to avoid the nonidentifiability problem on APC models but to show which of them are, in fact, applied in the literature, especially in the cancer mortality studies. A search strategy was made to identify evidence on MEDLINE (PubMed), Scopus, EMBASE, Science Direct and Web of Science. link3 A total of 46 papers were analyzed. The main methods found were Holford's method (n = 14; 30%), ntrinsic estimator (n = 10; 22%), Osmond & Gardner method n = 8; 17%), Carstensen (n = 6;13%), Bayesian approach (n = 6;13%) and others (n = 2; 5%). Even with their limitations, all methods have beneficial applications. However, the decision to use one or another method seemed to be more related to an observed geographic pattern.
