Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Applying Reinforcement Learning in Treatment Strategies for Cardiogenic Shock Patients

Objectius de Desenvolupament Sostenible::3 - Salut i Benesta

Exploration, design and application of simulation based technology in interventional cardiology

Medical education is undergoing a vast change from the traditional apprenticeship model to technology driven delivery of training to meet the demands of the new generation of doctors. With the reduction in the training hours of junior doctors, technology driven education can compensate for the time deficit in training. Each new technology arrives on a wave of great expectations; sometimes our expectations of true change are met and sometimes the new technology remains as a passing fashion only. The aim of the thesis is to explore, design and apply simulation based applications in interventional cardiology for educating the doctors and the public. Chapters 1and 2 present an overview of the current practice of education delivery and the evidence concerning simulation based education in interventional cardiology. Introduction of any new technology into an established system is often met with resistance. Hence Chapters 3 and 4 explore the attitudes and perceptions of consultants and trainees in cardiology towards the integration of a simulation based education into the cardiology curriculum. Chapters 5 and 6 present the “i-health project,” introduction of an electronic form for clinical information transfer from the ambulance crew to the hospital, enactment of case scenarios of myocardial infarction of varied levels of difficulty in a simulated environment and preliminary evaluation of the simulation. Chapter 7 focuses on educating the public in cardiovascular diseases and in coronary interventional procedures through simulation technology. Finally, Chapter 8 presents an overview of my findings, limitations and the future research that needs to be conducted which will enable the successful adoption of simulation based education into the cardiology curriculum.Open Acces

Improving implant training for physicians and their teams in under-represented regions

The burden of cardiovascular disease is increasing globally, with low- and middle-income countries (LMICs) absorbing most of the burden while lacking the necessary healthcare infrastructure to combat the increase. In particular, the disparity in pacemaker implants between high-income countries and LMICs is glaring, partially spurned by reduced numbers of physicians and supporting staff who are trained in pacemaker implant technique. Herein, we will discuss current pacemaker implant training models, outline training frameworks that can be applied to underserved regions, and review adjunctive training techniques that can help supplement traditional training models in LMICs

Space cabin atmospheres

Space cabin atmospheres - physical and physiological variable

Data-driven carbohydrate counting accuracy monitoring: A personalized approach

Accurate carbohydrate counting is crucial for type 1 diabetes mellitus patients on intensive insulin therapy to get on-target blood glucose values. So, it is fundamental to assess their ability to estimate meals’ carbohydrate content and, if needed, recommend carbohydrate counting training. In this context, we propose a personalized data-driven approach to monitor the patients’ ability to estimate the carbohydrate content of meals. The proposed approach uses personalized data to compute a safe range for the carbohydrate counting error according to the characteristics of each patient and adjust this interval to the patient's daily routines and food habits. Initially, the proposed method uses the insulin-to-carbohydrate ratio, the insulin sensitivity factor, the blood glucose limits, and the blood glucose target to compute a safe interval for the carbohydrate counting error, so the patient could train to reach this goal. Then, the app uses collected daily life data (i.e., blood glucose, meals carbohydrates content, and insulin bolus) to adjust the initial safe interval for the carbohydrate counting error according to the patient's needs. Preliminary assessment using the FDA-approved University of Virginia (UVA)/Padova Type 1 Diabetes Simulator shows the potential of the proposed approach to help type 1 diabetes patients being aware of their needs for carbohydrate counting education and how accurate they should be to achieve suitable blood glucose levels. Therefore, this tool has the potential to be a great asset to healthcare professionals and patients, improving the carbohydrate counting learning outcomes and leading to better glycemic control.publishe

Performances of low level hospital health caregivers after a neonatal resuscitation course

Background: High fidelity simulation has been executed to allow the evaluation of technical and non-technical skills of health caregivers. Our objective was to assess technical and non-technical performances of low level hospitals health caregivers who attended a Neonatal Resuscitation course using high fidelity simulation in a standard-setting scenario. Methods: Twenty-three volunteers were asked to manage a simple scenario (infant with secondary apnea) after the course. Technical and non-technical skills were assessed by using previously published scores. Performances were assessed during the scenario and after 2 months by filmed video recordings. Results: Sixteen (69.5%) participants failed to pass the minimum required technical score. Staff experience and participation in previous courses were associated to higher score in technical and non-technical skills, while working in level I or II hospitals did not affect the scores. Previous experience in neonatal resuscitation requiring positive pressure ventilation was associated to better non-technical performance. Technical and non-technical scores were significantly correlated (r = 0.67, p = 0.0005). Delayed and direct evaluation of technical skills provided the same scores. Conclusions: A neonatal resuscitation course, performed by using a high fidelity simulation manikin, had a limited impact on technical and non-technical skills of participants working in low level hospitals. Training programs should be tailored to the participants\u2019 professional background and to the more relevant sessions

Focal Spot, Winter 2005/2006

https://digitalcommons.wustl.edu/focal_spot_archives/1101/thumbnail.jp

Cardiopulmonary responses to maximal aerobic exercise in patients with cystic fibrosis (article)

This is the final version. Available from Public Library of Science via the DOI in this record.The dataset associated with this article is located in ORE at: https://doi.org/10.24378/exe.1105Cystic fibrosis (CF) is a debilitating chronic condition, which requires complex and expensive disease management. Exercise has now been recognised as a critical factor in improving health and quality of life in patients with CF. Hence, cardiopulmonary exercise testing (CPET) is used to determine aerobic fitness of young patients as part of the clinical management of CF. However, at present there is a lack of conclusive evidence for one limiting system of aerobic fitness for CF patients at individual patient level. Here, we perform detailed data analysis that allows us to identify important systems-level factors that affect aerobic fitness. We use patients’ data and principal component analysis to confirm the dependence of CPET performance on variables associated with ventilation and metabolic rates of oxygen consumption. We find that the time at which participants cross the gas exchange threshold (GET) is well correlated with their overall performance. Furthermore, we propose a predictive modelling framework that captures the relationship between ventilatory dynamics, lung capacity and function and performance in CPET within a group of children and adolescents with CF. Specifically, we show that using Gaussian processes (GP) we can predict GET at the individual patient level with reasonable accuracy given the small sample size of the available group of patients. We conclude by presenting an example and future perspectives for improving and extending the proposed framework. The modelling and analysis have the potential to pave the way to designing personalised exercise programmes that are tailored to specific individual needs relative to patient’s treatment therapies.Wellcome TrustEngineering and Physical Sciences Research Counci