Real world hospital costs following stress echocardiography in the UK: a costing study from the EVAREST/BSE-NSTEP multi-centre study

Background: Stress echocardiography is widely used to detect coronary artery disease, but little evidence on downstream hospital costs in real-world practice is available. We examined how stress echocardiography accuracy and downstream hospital costs vary across NHS hospitals and identified key factors that affect costs to help inform future clinical planning and guidelines. Methods: Data on 7636 patients recruited from 31 NHS hospitals within the UK between 2014 and 2020 as part of EVAREST/BSE-NSTEP clinical study, were used. Data included all diagnostic tests, procedures, and hospital admissions for 12 months after a stress echocardiogram and were costed using the NHS national unit costs. A decision tree was built to illustrate the clinical pathway and estimate average downstream hospital costs. Multi-level regression analysis was performed to identify variation in accuracy and costs at both patient, procedural, and hospital level. Linear regression and extrapolation were used to estimate annual hospital cost-savings associated with increasing predictive accuracy at hospital and national level. Results: Stress echocardiography accuracy varied with patient, hospital and operator characteristics. Hypertension, presence of wall motion abnormalities and higher number of hospital cardiology outpatient attendances annually reduced accuracy, adjusted odds ratio of 0.78 (95% CI 0.65 to 0.93), 0.27 (95% CI 0.15 to 0.48), 0.99 (95% CI 0.98 to 0.99) respectively, whereas a prior myocardial infarction, angiotensin receptor blocker medication, and greater operator experience increased accuracy, adjusted odds ratio of 1.77 (95% CI 1.34 to 2.33), 1.64 (95% CI 1.22 to 2.22), and 1.06 (95% CI 1.02 to 1.09) respectively. Average downstream costs were £646 per patient (SD 1796) with significant variation across hospitals. The average downstream costs between the 31 hospitals varied from £384–1730 per patient. False positive and false negative tests were associated with average downstream costs of £1446 (SD £601) and £4192 (SD 3332) respectively, driven by increased non-elective hospital admissions, adjusted odds ratio 2.48 (95% CI 1.08 to 5.66), 21.06 (95% CI 10.41 to 42.59) respectively. We estimated that an increase in accuracy by 1 percentage point could save the NHS in the UK £3.2 million annually. Conclusion: This study provides real-world evidence of downstream costs associated with stress echocardiography practice in the UK and estimates how improvements in accuracy could impact healthcare expenditure in the NHS. A real-world downstream costing approach could be adopted more widely in evaluation of imaging tests and interventions to reflect actual value for money and support realistic planning

Identifying the underlying factors affecting the development of participation of student sport in Iran

The purpose of this study was to identify the underlying factors that have an effect on the development of participation of student sport in Iran. The research method is qualitative and with Grounded Theory approach and it is inductive and exploratory. The research area consisted of 20 experts in sport management and student sport. The sampling method used a snowball based on the theoretical approach and continued until the categories reached theoretical saturation. The research tool was in-depth and exploratory semi-structured interviews. The validity of the findings was determined by matching methods by peer members and experimental interviews. Data were analysed using open, axial and selective coding using MAXQDA 2018 software. According to the results of the research, the underlying factors that have an effect on the development of participation of student sport were identified and extracted in nine main categories including community dynamism, synergy, financing, provide education, program-oriented activities, social capital, promoting participation culture, role-playing of managers, extracurricular activities in the field of student sport. Thus, student sport policymakers can use the concepts, components, and categories identified in their future plans to promote the status of school sports, especially in the international arena, and promote community health and to have a healthy and dynamic lifestyle, as well as the synergy of organizations and institutions to promote student sport

A Fuzzy Linguistic Multi-agent Model for Information Gathering on the Web Based on Collaborative Filtering Techniques

Information gathering in Internet is a complex activity. A solution consists in to assist Internet users in their information gathering processes by means of distributed intelligent agents in order to find the fittest information to their information needs. In this paper we describe a fuzzy linguistic multi-agent model that incorporates information filtering techniques in its structure, i.e., a collaborative filtering agent. In such a way, the information filtering possibilities of multi-agent system on the Web are increased and its retrieval results are improved.Research Project TIC2003-0797

On Using Multi-agent Systems in End to End Adaptive Monitoring

International audienceThe complexity (in terms of services and multimedia streaming) and dynamicity of telecommunication networks are continually growing, making network management and control more and more difficult. Such a management must be adaptive, dynamic and smart. In order to be sure of getting the best management mechanisms’ choice, a monitoring operation becomes fundamental. This paper presents a new adaptive monitoring approach based on a multi-agent system, taking into consideration the features of the networks namely flexibility, dynamicity, heterogeneity of users generated traffic, etc. A two-layers monitoring architecture is proposed in this paper. The first layer is responsible for dealing with monitoring of some local parameters, which are useful for the second layer. Indeed, this latter is adaptive, autonomous and able to make new decisions- about the data to monitor and the management mechanisms to activate- based on the current node’s state