Aerodynamic analysis of hospital ventilation according to seasonal variations. A simulation approach to prevent airborne viral transmission pathway during Covid-19 pandemic.

During the Covid-19 pandemic, location of the SARS-CoV-2 infected patients inside the hospital is a major issue to prevent viral cross-transmission. The objective of this study was to evaluate the risk of contamination through aerosol by using a global approach of the multiple environmental parameters to simulate, including seasonal context. A computational fluid dynamic (CFD) simulation based on the Lattice Boltzmann Method approach was used to predict airflow on the entire floor of a private hospital in Paris. The risk of contamination outside the rooms was evaluated by using a water vapor mass fraction tracker. Finally, the air contamination was estimated by a "cough model" producing several punctual emissions of contaminated air from potentially infected patients. In a winter configuration, the simulation showed a well-balanced ventilation on the floor and especially inside the rooms. After cough emissions from COVID-positive rooms, no significant contamination was observed in the circulation area, public waiting space and nurse office. On the contrary, in a summer configuration, the temperature difference due to the impact of the sun radiation between both sides of the building created additional air transport increasing the contamination risk in neighboring rooms and public spaces. Airborne spread was limited to rooms during winter conditions. On the contrary, during summer conditions, market airflow with potentially contaminated air coming from rooms located on the side of the building exposed to solar radiation was evidenced. These observations have implications to locate infected patients inside the building and for the conception of future health care structures.

Population pharmacokinetics of everolimus in cardiac recipients: comedications, ABCB1, and CYP3A5 polymorphisms.

BACKGROUND: The aim of this study was, using routine drug monitoring data, to identify patient characteristics that may influence everolimus (EVE) pharmacokinetic parameters and to develop a population pharmacokinetic model to predict EVE whole blood concentrations in cardiac recipients. METHODS: Fifty-nine patients were enrolled in the prospective study. Patient's characteristics were recorded including biological covariates and treatments. CYP3A5 and ABCB1 polymorphisms were determined. Seven hundred seventy-five EVE blood samples were collected for routine drug monitoring. Population pharmacokinetic modeling was carried out using the nonlinear mixed-effects modeling program. Results were analyzed according to a 1-compartment pharmacokinetic model with linear absorption and elimination. The model was evaluated using a bootstrap method and a visual predictive check procedure. RESULTS: The pharmacokinetic of EVE in cardiac recipients was best described by a 1-compartment model. Interindividual variability was best described by an exponential error model and residual error by a proportional plus additive error model. Estimation of EVE apparent clearance (3.33 ± 0.20 L/h) and apparent volume of distribution (146 ± 33 L) were in accordance with previously published data. Bilirubinemia and cyclosporine significantly influenced EVE clearance. Some covariates that were expected to influence EVE clearance, for example, ABCB1 and CYP3A5 polymorphisms, were not evidenced. No covariates influenced the volume of distribution of EVE. CONCLUSIONS: This study is the first population pharmacokinetic model of EVE in heart transplantation patients. It allows a better description of the pharmacokinetics of EVE. The present population pharmacokinetic model allows estimating a priori and a posteriori EVE concentrations in cardiac recipients and could limit the over and under drug exposure in this population.