Conectar
Endovascular aortic aneurysm repair (EVAR) requires long-term surveillance to detect and treat postoperative complications. However, prediction models to optimise follow-up strategies are still lacking. The primary objective of this study is to develop predictive models of post-operative outcomes following elective EVAR using Artificial Intelligence (AI)-driven analysis. The secondary objective is to investigate morphological aortic changes following EVAR.
This international, multicentre, observational study will retrospectively include 500 patients who underwent elective EVAR. Primary outcomes are EVAR postoperative complications including deaths, re-interventions, endoleaks, limb occlusion and stent-graft migration occurring within 1 year and at mid-term follow-up (1 to 3 years). Secondary outcomes are aortic anatomical changes. Morphological changes following EVAR will be analysed and compared based on preoperative and postoperative CT angiography (CTA) images (within 1 to 12 months, and at the last follow-up) using the AI-based software PRAEVAorta 2 (Nurea). Deep learning algorithms will be applied to stratify the risk of postoperative outcomes into low or high-risk categories. The training and testing dataset will be respectively composed of 70% and 30% of the cohort.
The study protocol is designed to ensure that the sponsor and the investigators comply with the principles of the Declaration of Helsinki and the ICH E6 good clinical practice guideline. The study has been approved by the ethics committee of the University Hospital of Patras (Patras, Greece) under the number 492/05.12.2024. The results of the study will be presented at relevant national and international conferences and submitted for publication to peer-review journals.
Sudden unexpected infant death (SUID) is a leading cause of infant mortality in the United States. Hospitals have implemented infant safe sleep programs with varying measures and degrees of success, but few have demonstrated improvements in hospital-based and home safe sleep practices with nurse subject matter experts (SMEs) and community SUID prevention campaigns.
This project evaluated the impact of a state-wide, evidence-based infant safe sleep program for birthing hospitals using nurse SMEs and a community awareness campaign on nurse knowledge, safe sleep environments, and trends in infant sleep-related deaths.
Between 2016 and 2021, a pre- and post-test quality improvement intervention-based design was used to enroll hospitals and train and embed SMEs to educate peers, conduct practice surveillance and audits, and address practice deviations. A website housed comprehensive resources, and a large-scale community-based social and print media campaign on safe sleep practices occurred. Nurse and practice data from 12 hospitals that fully implemented the program were compared pre- and post-implementation. State-wide survey data for key safe sleep indicators reported by parents were compared from our 12 birthing hospitals to other facilities.
Of trained nurses (N = 902), 83.4% reported making substantial or exceptional progress in being proactive in surveillance of safe sleep environments. Pre- and post-implementation environmental audits showed a significantly higher proportion of infants in safe sleep positions post-implementation (94.3%) than pre-implementation (89.6%) (p = 0.001). Statewide survey data from birth parents discharged from our program hospitals significantly outperformed those discharged from other state facilities. Multi-media campaigns resulted in over 1.4 million impressions on our website. Sleep-related deaths for infants born at four program hospitals dropped 16.1% from 31 in 2018 to 26 in 2021.
A safe sleep program improved hospital-based nurses' knowledge and practice and birth parent's knowledge and behaviors, and it was associated with a decrease in infant sleep-related deaths.
To review the current evidence on mixed reality (MR) applications in nursing practice, focusing on efficiency, ergonomics, satisfaction, competency, and team effectiveness.
Mixed methods systematic review of empirical studies evaluating MR interventions in nursing practice.
The systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered with PROSPERO. Studies were included if they assessed nursing outcomes related to MR interventions. Exclusion criteria encompassed reviews, studies focusing solely on virtual reality, and those involving only nursing students. The Cochrane ROBINS-I, RoB 2, and CASP tools assessed the risk of bias and methodological quality.
A comprehensive search of 12 databases (MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, and others) covered literature published between January 2013 and January 2023.
Eight studies met inclusion criteria, exploring diverse MR implementations, including smart glasses and mobile applications, across various nursing specialisations. MR demonstrated potential benefits in efficiency, such as faster task completion and improved accuracy. Satisfaction outcomes were limited but indicated promise. Ergonomic challenges were identified, including discomfort and technical issues. Studies on competency showed mixed results, with some evidence of improved skill acquisition. Team effectiveness and health equity outcomes were underexplored.
While MR shows potential in enhancing nursing practice, evidence is heterogeneous and clinical relevance remains unclear. Further rigorous comparative studies are necessary to establish its utility and address barriers to adoption.
MR technology may enhance nursing efficiency, competency and satisfaction. Addressing ergonomic and technical challenges could optimise adoption and benefit patient care.
This review adheres to PRISMA guidelines.
No Patient or Public Contribution.
PROSPERO registration: #CRD42022324066
For transgender and gender-diverse (TGD) people, it is known that there is a lack of healthcare professionals with experience in trans healthcare. This may result in either inadequate provision of healthcare or in an increased seeking of adequate trans healthcare. Little is known about healthcare services utilisation and resulting costs in treatment-seeking TGD people with gender incongruence or gender dysphoria (GIC/GD). Therefore, the aim of this study was to determine the excess costs associated with GIC/GD in Germany.
In a secondary analysis, baseline data of a randomised controlled trial with a sample of TGD people with GIC/GD were combined with data of a telephone survey conducted in a representative sample of the general German population. The data sets were matched using entropy balancing. Self-reported healthcare services utilisation was valued by standardised unit costs for the German healthcare system, and absenteeism from work and unemployment were valued with the gross hourly wage of persons in manufacturing and services sectors.
TGD people with GIC/GD living at least 50 km outside Hamburg in the federal state Bremen, Mecklenburg-Western Pomerania, Lower Saxony or Schleswig Holstein and the German general adult population.
Treatment-seeking TGD people with GIC/GD (n=167) and people of the general German population (n=2811).
6-month excess healthcare costs and indirect costs from a societal perspective were calculated for the year 2020 using two-part models with logit specification for the first part and a generalised linear model with gamma family and log link function for the second part.
The total 6-month excess costs associated with GIC/GD from a societal perspective were estimated to be 672 (95% CI: –3315 to 4657; p=0.741) per person. The direct excess healthcare costs were estimated to be 2 (–1115 to 1119; p=0.977) and the indirect excess costs due to absenteeism from work and unemployment were 669 (–3031 to 4370; p=0.723) per person. The total excess costs associated with GIC/GD in trans men, trans women and non-binary people were estimated to be –5572 (–12 232 to 1088), 4238 (–1694 to 10 170) and 3041 (–4268 to 10 351) per person (all with p>0.05), respectively.
The total 6-month costs in TGD people with GIC/GD did not differ statistically significantly from the costs in the general German population. Indirect excess costs due to absenteeism from work accounted for the largest part of the excess costs associated with GIC/GD, yet with wide 95% CIs. Potential causes of absenteeism from work, such as experienced or expected discrimination, need to be identified and addressed so that TGD people can experience a healthy work environment.
FreshRSS 