Notes

Disclosure involving Conflicts appealing Among Sound system, Moderators, Speakers, as well as Discussants with Nationwide Injury Group meetings.
A cohort review was conducted at a central London tertiary care hospital trust on the prevalence of homelessness among human immunodeficiency virus (HIV)-positive inpatients over a year. Data were collected on the duration of inpatient stay, co-morbidities including acquired immune deficiency syndrome (AIDS)-defining illnesses, co-infections, initiation of antiretroviral therapy, CD4 cell count, HIV viral load and substance misuse. Homeless people were found to be at high risk for hepatitis C, mental health illness, substance misuse including injecting drug use, recurrent bacterial infections, AIDS-associated illnesses, lower CD4 cell counts and HIV viremia. They also had more missed HIV outpatient appointments. It was highlighted that a multidisciplinary approach in their care was necessary to address their needs and reduce the morbidity burden in this cohort.We aim to identify associations that may help support strategies to increase job satisfaction and reduce unscheduled time off work for nurses. Given current concerns regarding nursing workforce and retention, it is vital we identify strategies and factors which maintain job satisfaction, support staff retention and reduce unscheduled time off work. As part of a quality improvement project, we conducted and distributed an online anonymous survey. Likert scales were used to measure job satisfaction, perceived quality of care, wellbeing, and unscheduled time off work. We explored participation in project work of any kind in the preceding 12 months, and captured nursing experience and current area of practice (inpatient/outpatient). A total of 350 complete responses were analysed. Nurses engaged in research or Quality Improvement Projects (QIPs) were more likely to have higher perceived levels of patient care (p = 0.0001), wellbeing (p = 0.0001) and job satisfaction (p = 0.0001) and reported lower levels of unscheduled time off work (p = 0.0001). Nurses engaged in research or quality improvement projects reported higher levels of job satisfaction, wellbeing, perceived higher levels of care in their workplace, and had lower levels of unscheduled time off work. We suggest that involving nurses in research/QIPs may improve workforce instability and job satisfaction.Patient-specific computer simulations can be a powerful tool in clinical applications, helping in diagnostics and the development of new treatments. However, its practical use depends on the reliability of the models. The construction of cardiac simulations involves several steps with inherent uncertainties, including model parameters, the generation of personalized geometry and fibre orientation assignment, which are semi-manual processes subject to errors. Thus, it is important to quantify how these uncertainties impact model predictions. The present work performs uncertainty quantification and sensitivity analyses to assess the variability in important quantities of interest (QoI). Clinical quantities are analysed in terms of overall variability and to identify which parameters are the major contributors. The analyses are performed for simulations of the left ventricle function during the entire cardiac cycle. Uncertainties are incorporated in several model parameters, including regional wall thickness, fibre orientation, passive material parameters, active stress and the circulatory model. The results show that the QoI are very sensitive to active stress, wall thickness and fibre direction, where ejection fraction and ventricular torsion are the most impacted outputs. Thus, to improve the precision of models of cardiac mechanics, new methods should be considered to decrease uncertainties associated with geometrical reconstruction, estimation of active stress and of fibre orientation. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. read more This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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