33 research outputs found
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Powered Respirators Are Effective, Sustainable, and Cost-Effective Personal Protective Equipment for SARS-CoV-2
Objectives: The provision of high-quality personal protective equipment (PPE) has been a critical challenge during the COVID-19 pandemic. We evaluated an alternative strategy, mass deployment of a powered air-purifying respirator (PeRSo), in a large university hospital.
Methods: We performed prospective user feedback via questionnaires sent to healthcare workers (HCWs) issued PeRSos, economic analysis, and evaluated the real-world impact.
Results: Where paired responses were available, PeRSo was preferred over droplet precautions for comfort, patient response, overall experience, and subjective feeling of safety. For all responses, more participants reported the overall experience being rated âVery goodâ more frequently for PeRSo. The primary limitation identified was impairment of hearing. Economic simulation exercises revealed that the adoption of PeRSo within ICUis associated with net cost savings in the majority of scenarios and savings increased progressively with greater ITU occupancy. In evaluation during the second UK wave, over 3,600 respirators were deployed, all requested by staff, which were associated with a low staff absence relative to most comparator hospitals.
Conclusions: Health services should consider a widespread implementation of powered reusable respirators as a safe and sustainable solution for the protection of HCWsas SARS-CoV-2 becomes an endemic viral illness
Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluidâdynamics model of the pulmonary circulation
The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluidâdynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patientâspecific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closedâform solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of stateâofâtheâart MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The longâterm goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system