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Effects of patient room layout on viral accruement on healthcare professionals' hands

2021; Wiley; Volume: 31; Issue: 5 Linguagem: Inglês

10.1111/ina.12834

1600-0668

Amanda M. Wilson, Marco‐Felipe King, Martín López‐García, I. Clifton, Jessica Proctor, Kelly A. Reynolds, Catherine J. Noakes,

Climate Change and Health Impacts

Indoor AirVolume 31, Issue 5 p. 1657-1672 ORIGINAL ARTICLEFree Access Effects of patient room layout on viral accruement on healthcare professionals' hands Amanda M. Wilson, Corresponding Author Amanda M. Wilson amwilson2@arizona.edu orcid.org/0000-0003-3259-8169 Rocky Mountain Center for Occupational and Environmental Health, University of Utah, Salt Lake City, UT, USA Department of Family and Preventive Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA Correspondence Amanda M. Wilson, Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, USA. Email: amwilson2@arizona.eduSearch for more papers by this authorMarco-Felipe King, Marco-Felipe King orcid.org/0000-0001-7010-476X School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this authorMartín López-García, Martín López-García orcid.org/0000-0003-3833-8595 School of Mathematics, University of Leeds, Leeds, UKSearch for more papers by this authorIan J. Clifton, Ian J. Clifton The Leeds Regional Adult Cystic Fibrosis Centre, St. James's University Hospital, Leeds Teaching Hospital NHS Trust, Leeds, UKSearch for more papers by this authorJessica Proctor, Jessica Proctor School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this authorKelly A. Reynolds, Kelly A. Reynolds orcid.org/0000-0003-4682-8359 Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USASearch for more papers by this authorCatherine J. Noakes, Catherine J. Noakes School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this author Amanda M. Wilson, Corresponding Author Amanda M. Wilson amwilson2@arizona.edu orcid.org/0000-0003-3259-8169 Rocky Mountain Center for Occupational and Environmental Health, University of Utah, Salt Lake City, UT, USA Department of Family and Preventive Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA Correspondence Amanda M. Wilson, Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, USA. Email: amwilson2@arizona.eduSearch for more papers by this authorMarco-Felipe King, Marco-Felipe King orcid.org/0000-0001-7010-476X School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this authorMartín López-García, Martín López-García orcid.org/0000-0003-3833-8595 School of Mathematics, University of Leeds, Leeds, UKSearch for more papers by this authorIan J. Clifton, Ian J. Clifton The Leeds Regional Adult Cystic Fibrosis Centre, St. James's University Hospital, Leeds Teaching Hospital NHS Trust, Leeds, UKSearch for more papers by this authorJessica Proctor, Jessica Proctor School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this authorKelly A. Reynolds, Kelly A. Reynolds orcid.org/0000-0003-4682-8359 Department of Community, Environment, & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USASearch for more papers by this authorCatherine J. Noakes, Catherine J. Noakes School of Civil Engineering, University of Leeds, Leeds, UKSearch for more papers by this author First published: 29 April 2021 https://doi.org/10.1111/ina.12834Citations: 2 Funding information: M-F. King and C.J. Noakes were funded by the Engineering and Physical Sciences Research Council, UK: Healthcare Environment Control, Optimization and Infection Risk Assessment (https://HECOIRA.leeds.ac.uk) (EP/P023312/1). M. López-García was funded by the Medical Research Council, UK (MR/N014855/1). J. Proctor was funded by EPSRC Centre for Doctoral Training in Fluid Dynamics at Leeds (EP/L01615X/1) AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Healthcare professionals (HCPs) are exposed to highly infectious viruses, such as norovirus, through multiple exposure routes. Understanding exposure mechanisms will inform exposure mitigation interventions. The study objective was to evaluate the influences of hospital patient room layout on differences in HCPs' predicted hand contamination from deposited norovirus particles. Computational fluid dynamic (CFD) simulations of a hospital patient room were investigated to find differences in spatial deposition patterns of bioaerosols for right-facing and left-facing bed layouts under different ventilation conditions. A microbial transfer model underpinned by observed mock care for three care types (intravenous therapy (IV) care, observational care, and doctors' rounds) was applied to estimate HCP hand contamination. Viral accruement was contrasted between room orientation, care type, and by assumptions about whether bioaerosol deposition was the same or variable by room orientation. Differences in sequences of surface contacts were observed for care type and room orientation. Simulated viral accruement differences between room types were influenced by mostly by differences in bioaerosol deposition and by behavior sequences when deposition patterns for the room orientations were similar. Differences between care types were likely driven by differences in hand-to-patient contact frequency, with doctors' rounds resulting in the greatest predicted viral accruement on hands. Practical Implications Understanding spatial deposition of bioaerosols containing norovirus and the influence of space on human behavior is crucial to increasing accuracy of predicting exposure on hands and subsequent infection risks from self-inoculation behaviors. As demonstrated in the simulations in this work, the timing of glove donning/doffing and hand sanitizer use can have important implications for their ability to protect healthcare workers, especially considering hand-to-patient contacts. These models can inform the design of healthcare rooms and their ventilation as well as administrative controls, such as training that quantitatively illustrates concepts such as the importance of proper donning/doffing technique and the 5 moments for hand hygiene (which include after a patient contact) for lowering occupational microbial exposures. 1 INTRODUCTION Healthcare professionals (HCPs) face a number of unique occupational hazards including exposures to infectious agents that may be present in the work environment due to infected patients, visitors, coworkers, or contamination in the environment. In the United States, more than 18 million workers are in the healthcare industry, and as this number continues to increase, HCPs have some of the highest rates of occupationally related illness.1 Worldwide, the prioritization of the health of HCPs has been emphasized due to increased healthcare demands in response to the COVID-19 pandemic.2, 3 By July 16, 2020, the U.S. Centers for Disease Control and Prevention (CDC) reported HCPs accounting for approximately 4% (100 570 out of 2.5 million) of U.S. COVID-19 cases.4 However, the proportion of cases attributable to HCPs could be higher due to only having HCP status data for 22% of total reported cases.4 In a study of 120 075 UK essential and non-essential workers, HCPs had a 7.43 (95% CI: 5.52, 10.00) times greater risk of severe COVID-29 relative to non-essential workers.5 This risk ratio was greater than that of “social and educational workers” and of “other essential workers” relative to non-essential workers.5 Even outside of pandemic conditions, HCPs may be regularly exposed to other highly infectious agents, such as norovirus, a non-enveloped, single-stranded RNA enteric virus6, 7 that is generally spread via a fecal-oral pathway and can be transmitted via person-to-person, fomite, and airborne routes where aerosols are inhaled into the mouth.8-10 Healthcare workers have been shown to be at high risk for norovirus infection during outbreaks in occupational settings.11 Norovirus infection of HCPs can lead to not only health risks and loss of time at work but also risks to patients, especially considering the potential for asymptomatic infection and high viral shedding.11 Analysis of the burden of norovirus in UK hospitals over a 3-year period suggests an annual median of 290 000 bed-days was attributable to norovirus, displacing 57 800 other patients and costing £107.6 million.12 The same study analyzed reported data on the impacts on HCPs, estimating that a median of 4200 members of staff was absent annual during norovirus outbreaks. While norovirus has been shown to be transmitted via a fomite route, exposure routes in the environment are often interconnected, where norovirus on fomites may originate from bioaerosol deposition. Bioaerosols may originate from vomit or fecal shedding events. In this way, exposures via air, surfaces, and direct person-to-person contact (such as contacts between HCPs and patients) are a part of a larger system contributing to exposure. The potential for fomite contamination spread via hand-to-surface contacts, especially for HCPs, has been a long-recognized mechanism of nosocomial disease transmission.13, 14 The frequency and sequence of contacts with different surface types,15, 16 for different care types during simulated vs. actual procedures,16, 17 and the effect of these differences on microbial exposures have been explored.18 However, it is unknown how spatial differences between patient rooms may affect deposition patterns, hand-to-surface behaviors of healthcare professionals, and subsequent exposures. Understanding the influence of spatial differences on behavior and contamination spread via the air-surface interface is important for advancing efforts for developing environment-specific infection control protocols. 1.1 Study objective The objective of this study was to evaluate the influence of differences in HCP behavior and differences in airflow and subsequent bioaerosol deposition on surfaces for two single-patient room layouts on norovirus accruement on HCP hands. A secondary objective was to demonstrate how a calibrated microbial transfer model can be utilized in exposure modeling. To meet these objectives, an integrated exposure model composed of a finite volume Navier-Stokes computational fluid dynamics (CFD) model using Lagrangian particle tracking,19 a human behavior model informed by real-world data,17 and a viral transfer model calibrated for representation of transfer of enteric viruses20 was developed. 2 METHODS 2.1 Behavior observations and simulation of behaviors Hand-to-surface and hand hygiene events (glove donning/doffing and hand sanitizer use) were recorded for healthcare professionals in single-patient rooms conducting mock IV-care, observational care, or doctors' rounds. A hand-to-surface contact event was defined as a single hand making physical contact with the object. Details regarding behavioral observations have been described by King et al.16 Discrete Markov chains informed by observed behaviors were used to simulate sequences of hand-to-surface contacts, glove donning/doffing, or hand hygiene, as has been done in other healthcare worker behavior modeling.21 Six transitional probability matrices were created for right- and left-facing rooms for observational care, IV-drip care, and doctors' rounds using the function “markovchainFit” from the R statistical software package, markovchain. For each probability matrix, behavior states included entrance into patient room, exit from patient room, use of alcohol gel, hand-to-equipment contact, hand-to-far-patient surface contact, hand-to-near-patient surface contact, hand-to-patient contact, doffing of gloves, donning of gloves, and hand-to-hygiene surface contact. Categories of surfaces matching these surface type designations for categorizing observed behaviors have been described previously by King et al.17 Transitional probability matrices were altered so that exit from patient room was an absorbing state and the probability of an “entrance into patient room” event after the initial entrance was zero. When generating behavior sequences, each sequence began with entrance into the patient room. New events would be generated until exit from the patient room occurred. To evaluate the effect of iteration choice on mean accruement on hands over the number of contacts, mean concentrations on the right hand were compared for 1000; 5000; and 10 000 iterations per room type (left- and right-facing) and care type (IV-care, observational care, doctors' rounds) combination. There were no notable differences in mean concentration on the hand over the number of contacts between results for the 5000 and 10 000 iteration runs (Figures S1-S3). Therefore, 5000 iterations were used. 2.2 Exposure model scenarios In Scenario 1, the same concentrations of norovirus on surfaces were used regardless of patient bed orientation. Heterogeneity in concentrations between surfaces was informed by CFD simulations for the right-facing room orientation, and these results were then used for both the right- and left-facing rooms. Therefore, any differences between exposures by room orientation or procedure type could then be determined to be behavior driven. In Scenario 2, CFD was used to predict the likely effect of patient bed orientation and room layout on heterogeneous deposition of bioaerosols on surfaces of different surface types (near-patient vs. far-patient surfaces, for example). 2.3 Changes in norovirus concentration on hands During the contact, k, with a surface, a change in norovirus concentration on either a gloved or ungloved hand was estimated as a function of transfer efficiency (λ, in hand-to-surface and surface-to-hand directions), fraction of the hand in contact with the surface (SH), the concentration of norovirus on the surface (Csurface), and the concentration of norovirus on the hand before this contact (Chand,k−1) (viral particles/cm2) (Equation 1), an adapted version of a model by Julian et al. (2009).22 It was assumed HCP hands were uncontaminated at the start of care. (1) While asymmetrical transfer efficiencies have been reported for certain organisms, and it has been noted that assuming transfer efficiency is the same in both directions can result in substantial modeling errors,23-25 MS2 and PhiX174, enteric viruses, have been shown to transfer similarly from hand-to-surface and surface-to-hand.20, 24 Changes in concentration on surfaces were not tracked, as it was assumed that different portions of the same surface may be contacted and that deposited virus on that surface was spread homogeneously across the entire surface area. Inactivation of virus was not incorporated, as non-enveloped viruses can persist in the environment for longer periods relative to the duration of episodes of care. For example, Fedorenko et al. (2020) demonstrated that MS2 and PhiX174, non-enveloped enteric viruses, in evaporated saliva microdroplets on a glass surface only reduced by approximately 1 log10 over a 14-hour period for a range of relative humidities.26 By comparison, observed mock care episodes used to inform simulated behaviors in this study ranged from 0.6 to 11.7 minutes.17 2.4 Transfer efficiency Values for transfer efficiency () were informed by a probability distribution calibrated to the model through previous work relevant for hand-to-surface contacts and enteric viral exchange between two contaminated surfaces.20 It is acknowledged that these transfer efficiencies are not specific to the wide variety of surfaces anticipated in this exposure scenario. However, to our knowledge, other transfer efficiencies available in the literature27, 28 are limited in that they do not account for both surfaces being contaminated. While the first contact in the simulation assumes an uncontaminated hand contacts a surface, following contacts involve exchange of norovirus between surfaces and hands. Since this distribution was calibrated for hand-to-surface contacts, specifically, a different value was used for hand-to-patient contacts. King et al. found that Escherichia coli transfer efficiencies for ungloved contacts (49%, 95% CI = 32–72%) were higher than for gloved contacts (30%, 95% CI = 17–49%).29 This has been demonstrated for other organisms as well.23 Transfer efficiency for a gloved contact was therefore assumed to be 0.61 times smaller than the randomly sampled transfer efficiency from the posterior distribution of transfer efficiencies from Wilson et al.20 While microbial transfer between hand-to-hand contacts has been demonstrated, transfer efficiency values were not available for application in the microbial transfer model. Therefore, we assumed that transfer efficiency between the gloved or ungloved hand of a healthcare worker and the skin or clothing of a patient could span a wide range of transfer efficiencies. We assumed a uniform distribution with a minimum of 0.0001 and a maximum of 0.406, as these are minimum and maximum transfer efficiencies for MS2 reported by Lopez et al. (2013) that capture both nonporous and porous surfaces under low relative humidity conditions (15–32%).27 2.5 Fraction of total hand surface area of contact Different distributions to describe the fraction of the hand used per hand-to-surface contact () were used depending upon the contact type. For entrance and exit from the patient room, it was assumed that an open hand grip would be used. Therefore, a uniform distribution was randomly sampled with a minimum of 0.10 and a maximum of 0.21, the minimum and maximum SH of left and right hands measured by AuYeung et al.30 For patient contacts, it was assumed that a partial front palm without fingers up to a full front palm with fingers may be used.30 Therefore, a uniform distribution with a minimum of 0.03 and a maximum of 0.25 was randomly sampled, where these minimum and maximum values were informed by AuYeung et al.30 The fractions of the hand used for partial front palm without finger contact configurations are similar to those for front partial fingers,30 so this range includes values that could represent this configuration as well. For all other contacts, it was assumed that various grip and hand press contact types could be used, aside from hand immersion contacts described by AuYeung et al.30 Therefore, a uniform distribution with a minimum of 0.008 (the minimum of front partial fingers/5 fingers to represent a single fingertip contact) and a maximum of 0.25 (the maximum of full front palm with fingers) was used.30 2.6 Glove donning/doffing It was assumed at the start of the simulation that HCPs were not wearing gloves. If a glove event occurred, this was not donning or doffing specific, but, rather, the current state was changed from either gloved to ungloved or from ungloved to gloved. This prevented instances such as a glove donning event following a later glove donning event without an intermediary doffing event or sequential glove doffing events without an intermediary donning event. For hand hygiene events, it was ensured that this was under ungloved conditions. If a hand hygiene event was selected when gloves were on the hands, a new event was randomly sampled until a non–hand-hygiene event was selected. 2.7 Hand hygiene efficacy When a hand sanitizer event was selected and if gloves were not on, norovirus concentration on hands was reduced by an efficacy informed by Wilson et al., where efficacies with an alcohol-based hand sanitizer were measured with human norovirus for 30- and 60-second contact times.31 Due to a low sample size for efficacies reported by Wilson et al., a uniform distribution was used with a minimum (0.15 log10) and a maximum (2.07 log10) informed by minimum and maximum reductions for the nonresidual alcohol-based hand sanitizer for both 30- and 60-second contact times.31 If gloves were on for this hand hygiene moment, a new event was randomly sampled to replace the hand sanitizer event under the assumption that hand sanitizer would not be applied with gloves on. 2.8 Infection risk Infection risks were estimated to evaluate how differences in viral concentration on hands would relate to risk differences between care types and room orientations. Due to lack of sequence data to include hand-to-face contacts within the simulation, a single hand-to-face contact was assumed at the end of the simulation to estimate an infection risk based on the concentration on the hands at the end of the episode of care. Single hand-to-face contacts have been used in other exposure modeling studies to compare risks between different scenarios.32 However, it is acknowledged that these risks do not reflect those of reality, as they do not account for the timing and frequency of expected hand-to-face contacts and are only using these risks for comparison purposes. To estimate an infection risk, a viral dose was first estimated by multiplying a transfer efficiency, hand surface area, and fraction of the total hand surface area to be used by the concentration on the right or left hand, where either hand had a 50/50 chance of being chosen based on reported lack of differences in contact sequences for right and left hands in a micro-activity study.33 If no gloves were on, a transfer efficiency was randomly sampled from a normal distribution informed by Rusin et al.28 and left- and right-truncated at 0 and 1, respectively. If gloves were worn, these transfer efficiencies were reduced, consistent with how transfer efficiencies for hand-to-fomite contacts were handled, described above. Total hand surface area for a single hand was randomly sampled from a uniform distribution (min = 445 cm2, max = 535 cm2) informed by Beamer et al.34 and the U.S. EPA's Exposure Factors Handbook (2011).35 It was assumed a single fingertip or a fraction of the palm would be used for the contact, and this fraction of total hand surface area that this represents was randomly sampled from a uniform distribution (min = 0.006, max = 0.012). The minimum and maximum fractions of the hand that all fingertips represent reported by AuYeung et al.30 for adult hands were divided by 5 to inform the distribution. To relate these doses to infection risk, an approximate beta-Poisson curve was used, where α = 0.104 and β = 32.3 (Equation 2)36: (2) Although this curve is being used to estimate risks for comparison purposes, it is acknowledged that multiple dose-response curves for norovirus exist and should be considered when predicting risks for risk assessments.36 2.9 CFD methodology The CFD methodology by King et al.19 was closely followed. A steady-state simulation assuming isothermal conditions and natural ventilation from three windows open 10 cm with an air exchange rate of 6 was modeled using Fluent v.19.4 (ANSYS, Canonsburg, PA, USA). The door (pressure outlet) had a surface area of 1.9 m2, while the large window (velocity inlet) had a surface area of 0.18 m2 and the small windows (velocity inlets) each had a surface area of 0.08 m2. A velocity mesh sensitivity analysis was conducted with three sequentially size-halved cell sizes. A hex-dominant mesh with 4 cm element size and 2 cm cells was used for the bulk volume and close to surfaces, respectively. A k-omega transition shear stress transport turbulence model with standard omega wall function formulation was used. A point near the patient mouth was set as the inert water particle injection site, where particles were injected at a velocity of 1.9 m/s, in part informed by Tang et al.37 This is based on breathing due to a lack of data on velocity and aerosols associated with vomiting events, but is considered as representative of a small aerosol source from a person. We assume that large droplets and splashes would be cleaned immediately postevent, so are concerned about the surface contamination that may occur sometime later following the event. Addressing aerosol emissions due to breathing also increases the generalizability of this work, providing insights into how emissions of respiratory viruses via breathing may deposit on surfaces and contribute to fomite-mediated exposure routes as well. However, experimental data used to calibrate the microbial transfer model used in this integrated model more appropriately represent enteric viruses, such as norovirus. The particle size range (0.14–8.13 µm) was informed by Alsved et al.38 This range reflects a range of aerosols in which Alsved et al. detected norovirus.38 The particle diameter remained constant throughout the simulation, assuming that all particles were their fully evaporated size. Deposition of particles on surfaces was then tracked using a Lagrangian particle methodology with discrete random walk and trap boundary condition on surfaces, including the walls. The fraction of injected particles that landed on specific surface types were related to expected viral concentrations on surfaces by estimating a number of viral particles to be released by a patient, informed by Alsved et al. and the U.S. Environmental Protection Agency's Exposure Factors Handbook (2011).35, 38 The fraction of virus expected to land on each respective surface was then calculated, divided by the total surface area to obtain viral particles/cm2. Surface areas of surfaces are listed in Table S1. Sizes of particles were not tracked upon deposition, meaning that the fraction of deposited particles does not account for differences in particle size or virus concentrations across ranges of particle sizes. However, the distribution of particle sizes in this study was low, with most of the distribution of sizes being below 5 μm, meaning we would not expect as much error due to assuming homogeneous deposition of particle sizes across surfaces as if we considered a range of larger aerosol sizes in which larger aerosols may settle considerably faster than fine aerosols <5 μm. For the right-facing room orientation, estimated particle deposition on the desk was used to inform the concentration anticipated on far-patient and hygiene area surfaces. For the left-facing room orientation, surface concentrations on far-patient and hygiene area surfaces were informed by the concentration on the walls. For the right-facing room orientation, near-patient and equipment surface concentrations were informed by estimated particles deposited on the side table, bed, and chair, while for left-facing rooms, near-patient and equipment surface concentrations were also informed by deposition on the desk in addition to these other surfaces. For both room orientations, particles deposited on the patient were used to inform concentrations on the patient. The “in” and “out” event, entrance, and exit from the patient room, respectively, involved contact with the door handle. In this case, it was assumed that concentrations on the door handle were zero since the focus of this study was on fomite-mediated exposures as a result of particle deposition alone. 2.10 Exposure model sensitivity analysis Spearman correlation coefficients were calculated to quantify monotonic relationships between model inputs and the mean and maximum concentrations on hands. Since some parameters, such as transfer efficiency, surface concentration, and the fraction of the hand used for a contact, varied by contact, the mean value of these parameters per iteration was used. Spearman correlation coefficients were also calculated to investigate relationships between input parameters, since some inputs were related, where a greater amount of patient contacts could relate to a greater mean transfer efficiency since larger transfer efficiencies were used for hand-to-patient contacts than for hand-to-surface contacts, for example. Since some relationships between model inputs and mean or maximum viral concentration on hands may not be monotonic, scatter plots were also visually inspected. 2.11 Particle deposition sensitivity analysis In addition to the baseline model with a ventilation rate of 6 ACH and the windows acting a velocity inlet and the door acting as a pressure outlet, particle deposition patterns for a number of other scenarios were explored: the door acting as a velocity inlet and windows acting as a pressure outlet, the small windows acting as velocity inlets and the large window acting as a pressure outlet, and exploring 2.5 ACH and 10 ACH in addition to 6 ACH. This reflects guidance for UK hospitals which recommends 6 ACH for bedrooms and 10 ACH for treatment rooms and isolation rooms,39 and also recognizes that many hospitals, especially those that are older or naturally ventilated, have air change rates below the current standards. Mean viral concentrations on hands for left- and right-facing rooms were then compared for these 9 scenarios (3 ACHs × 3 velocity inlet, pressure outlet scenarios). 3 RESULTS 3.1 Deposition The predicted deposition of particles on surfaces between the left- and right-facing rooms in the primary model (6 ACH, windows as velocity inlets, door as pressure outlet) were notably different (Figure 1). The left-facing room resulted in 51.18% of emitted particles depositing on the patient, while the right-facing room