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Frontiers in Endocrinology 2023To predict the optimal cut-off values for screening and predicting metabolic syndrome(MetS) in a middle-aged and elderly Chinese population using 13 obesity and...
OBJECTIVE
To predict the optimal cut-off values for screening and predicting metabolic syndrome(MetS) in a middle-aged and elderly Chinese population using 13 obesity and lipid-related indicators, and to identify the most suitable predictors.
METHODS
The data for this cross-sectional investigation came from the China Health and Retirement Longitudinal Study (CHARLS), including 9457 middle-aged and elderly people aged 45-98 years old. We examined 13 indicators, including waist circumference (WC), body mass index (BMI), waist-height ratio (WHtR), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), lipid accumulation product index (LAP), conicity index (CI), Chinese visceral adiposity index (CVAI), triglyceride-glucose index (TyG-index) and their combined indices (TyG-BMI, TyG-WC, TyG-WHtR). The receiver operating characteristic curve (ROC) was used to determine the usefulness of indicators for screening for MetS in the elderly and to determine their cut-off values, sensitivity, specificity, and area under the curve (AUC). Association analysis of 13 obesity-related indicators with MetS was performed using binary logistic regression analysis.
RESULTS
A total of 9457 middle-aged and elderly Chinese were included in this study, and the overall prevalence of the study population was 41.87% according to the diagnostic criteria of NCEP ATP III. According to age and gender, the percentage of males diagnosed with MetS was 30.67% (45-54 years old: 30.95%, 55-64 years old: 41.02%, 65-74 years old: 21.19%, ≥ 75 years old: 6.84%). The percentage of females diagnosed with MetS was 51.38% (45-54 years old: 31.95%, 55-64 years old: 39.52%, 65-74 years old: 20.43%, ≥ 75 years old: 8.10%). The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS. ABSI had a poor prediction ability.
CONCLUSIONS
Among the middle-aged and elderly population in China, after adjusting for confounding factors, all the indicators except ABSI had good predictive power. The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS.
Topics: Aged; Aged, 80 and over; Female; Humans; Male; Middle Aged; Cross-Sectional Studies; East Asian People; Longitudinal Studies; Metabolic Syndrome; Obesity; Obesity, Abdominal; Risk Factors; Triglycerides
PubMed: 37576971
DOI: 10.3389/fendo.2023.1201132 -
Physician Assistant Clinics Jul 2023Viral infections are some of the most common sources of respiratory illness in pediatric and adult populations worldwide. Influenza and coronaviruses are viral pathogens... (Review)
Review
Viral infections are some of the most common sources of respiratory illness in pediatric and adult populations worldwide. Influenza and coronaviruses are viral pathogens that could lead to severe respiratory illness and death. More recently, respiratory illness from coronaviruses, accounts for more than 1 million deaths in the United States alone. This article will explore the epidemiology, pathogenesis, diagnosis, treatment, and prevention of severe acute respiratory syndrome caused by coronavirus-2, and Middle Eastern respiratory syndrome.
PubMed: 37197227
DOI: 10.1016/j.cpha.2023.03.002 -
International Journal of Nursing Studies Oct 2023Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium...
BACKGROUND
Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium among critically ill children are not well researched.
OBJECTIVE
To develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission.
DESIGN
A prospective cohort study.
SETTING
A large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022.
PARTICIPANTS
One thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h.
METHODS
Five machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC).
RESULTS
Of the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759-0.851), with 0.798 (95 % CI, 0.758-0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram.
CONCLUSIONS
Machine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.
Topics: Male; Humans; Child; Infant, Newborn; Female; Prospective Studies; Critical Illness; Delirium; Intensive Care Units, Pediatric; Hospitalization; Machine Learning
PubMed: 37542959
DOI: 10.1016/j.ijnurstu.2023.104565 -
Anales de Pediatria Aug 2023
Topics: Child; Humans; Pediatric Nursing; Workforce
PubMed: 37474416
DOI: 10.1016/j.anpede.2023.06.014 -
Minerva Medica Apr 2024
Topics: Humans; Eosinophilic Esophagitis; Child
PubMed: 37227238
DOI: 10.23736/S0026-4806.23.08626-3 -
Revista Brasileira de Enfermagem 2023to assess nursing students' and nurses' knowledge, satisfaction and self-confidence after a theoretical workshop on emergency care for traumatized children and clinical...
OBJECTIVE
to assess nursing students' and nurses' knowledge, satisfaction and self-confidence after a theoretical workshop on emergency care for traumatized children and clinical simulation.
METHODS
a quasi-experimental study, carried out with nursing students and nurses residing at a public university in southern Brazil. A workshop on pediatric trauma care was created and a mannequin was created for simulations. A knowledge pre-test and post-test and the Student Satisfaction and Self-Confidence in Learning instrument were applied to measure satisfaction and self-confidence in learning. For analysis, descriptive statistics and the Wilcoxon test were used to compare means before and after intervention.
RESULTS
the difference between misses and hits was statistically significant (p<0.005), demonstrating an increase in participants' knowledge after the workshop. Satisfaction and self-confidence were demonstrated in the instrument's high scores.
CONCLUSIONS
the effectiveness of the workshop in teaching-learning emergency care for pediatric trauma was demonstrated.
Topics: Humans; Child; Clinical Competence; Nurses; Learning; Education, Nursing, Baccalaureate; Students, Nursing; Emergency Medical Services
PubMed: 38088706
DOI: 10.1590/0034-7167-2021-0485 -
Nursing Open Dec 2023Nursing competencies are crucial indicators for providing quality and safe care. The lack of international agreement in this field has caused problems in the... (Review)
Review
AIM
Nursing competencies are crucial indicators for providing quality and safe care. The lack of international agreement in this field has caused problems in the generalization and application of findings. The purpose of this review is to identify the core competencies necessary for undergraduate nursing students to enter nursing work.
DATA SOURCES
We conducted a structured search using Scopus, MEDLINE (PubMed), Science Direct, CINAHL, Web of Science, and Google Scholar.
REVIEW METHODS
We conducted a scoping review using the methodology recommended by the Joanna Briggs Institute, supported by the PAGER framework, and guided by the PRISMA-ScR Checklist. Inclusion criteria included full-text articles in English, quantitative and qualitative research related to competencies for undergraduate students or newly graduated nurses, competency assessment, and tool development from 1970 to 2022. We excluded articles related to specific nursing roles, specific contexts, Master's and Ph.D. curricula, hospital work environment competencies, and editorial.
RESULTS
Out of 15,875 articles, we selected 43 studies, and data analysis with summative content analysis identified five themes named individualized care, professional nursing process, nursing administration, readiness, and professional development.
CONCLUSION
Considering the dynamics of competencies and their change with time, experience, and setting, it is necessary to update, localize, and levelling of the proposed competencies based on the culture of each country.
IMPACT
These competencies provide a guide for undergraduate nursing curriculum development and offer a framework for both clinical instruction and the evaluation of nursing students.
Topics: Humans; Education, Nursing, Baccalaureate; Students, Nursing; Curriculum; Qualitative Research; Generalization, Psychological
PubMed: 37817394
DOI: 10.1002/nop2.2020