ABSTRACT
BACKGROUND: Sepsis is a recognized global health challenge that places a considerable disease burden on countries. Although there has been some progress in the study of sepsis, the mortality rate of sepsis remains high. The relationship between serum osmolality and the prognosis of patients with sepsis is unclear. METHOD: Patients with sepsis who met the criteria in the Medical Information Mart for Intensive Care IV database were included in the study. Hazard ratios (HRs) and 95% confidence intervals (CIs) were determined using multivariable Cox regression. The relationship between serum osmolality and the 28-day mortality risk in patients with sepsis was investigated using curve fitting, and inflection points were calculated. RESULTS: A total of 13,219 patients with sepsis were enrolled in the study; the mean age was 65.1 years, 56.9 % were male, and the 28-day mortality rate was 18.8 %. After adjusting for covariates, the risk of 28-day mortality was elevated by 99% (HR 1.99, 95%CI 1.74-2.28) in the highest quintile of serum osmolality (Q5 >303.21) and by 59% (HR 1.59, 95%CI 1.39-1.83) in the lowest quintile (Q1 ≤285.80), as compared to the reference quintile (Q3 291.38-296.29). The results of the curve fitting showed a U-shaped relationship between serum osmolality and the risk of 28-day mortality, with an inflection point of 286.9 mmol/L. CONCLUSION: There is a U-shaped relationship between serum osmolality and the 28-day mortality risk in patients with sepsis. Higher or lower serum osmolality is associated with an increased risk of mortality in patients with sepsis. Patients with sepsis have a lower risk of mortality when their osmolality is 285.80-296.29 mmol/L.
ABSTRACT
Introduction: The association between sleep duration and cough, wheezing, and dyspnea was unclear. This research aimed to test this relationship. Methods: Research data were obtained from people who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2012. We used weighted logistic regression analysis and fitted curves to explore the association between sleep and respiratory symptoms. In addition, we investigated the association between sleep duration, chronic obstructive pulmonary disease (COPD), and asthma. The stratified analysis is used to analyze inflection points and specific populations. Results: The 14,742 subjects are weighted to reflect the 45,678,491 population across the United States. Weighted logistic regression and fitted curves show a U-shaped relationship between sleep duration and cough and dyspnea. This U-shaped relationship remained in people without COPD and asthma. The stratified analysis confirmed that sleep duration before 7.5 h was negatively associated with cough (HR 0.80, 95% CI 0.73-0.87) and dyspnea (HR 0.82, 95% CI 0.77-0.88). In contrast, it was positively associated with cough and (HR 1.30, 95% CI 1.14-1.48) dyspnea (HR 1.12, 95% CI 1.00-1.26) when sleep duration was >7.5 h. In addition, short sleep duration is associated with wheezing, asthma, and COPD. Conclusion: Both long and short sleep duration are associated with cough and dyspnea. And short sleep duration is also an independent risk factor for wheezing, asthma, and COPD. This finding provides new insights into the management of respiratory symptoms and diseases.
ABSTRACT
Over the last 20 years, big data techniques in teaching have been overgrown. Making educational decisions now includes information knowledge as a crucial component. This started a trend for using big data algorithms strategically. Technological advances have been used to analyze the enormous amount of information and develop strategic judgments. The current study aims to address issues with the conventional instructional, administrative management solution focused on manual rule formulation in big data storage and interpretation and has poor efficiency in analyzing big data and lacking value in developing education leadership qualities. The study suggests an educational leadership model based on big data algorithm (ELM-BDA) to explore the student leadership performance that relies on cooperative filtration and fuzzy C-means (FCM) and big data. The different influencing mechanisms and factors directly linked to educational leadership were also analyzed using a big data algorithm. To build an intelligent institutional administrative system, the research also exposes it to organizational studies. By evaluating the big data research already in existence, this study emphasizes the expanding significance of big data. Additionally, this study explores the effects of big data analytics on educational leadership qualities by utilizing an FCM. A scoring system is designed to predict the student's leadership level, and using the big data algorithms, the students are motivated and trained to improve their skills. The education and learning method can be enhanced at educational institutions through better decision-making to use this big data for leadership development. Big data facilitates efficient educational decision-making by merging various data and telecommunications technologies. Using big data in schooling will increase-leadership quality among students. To effectively use big data for decision-making, academic leaders must create new types of learning and monitoring systems.
