key: cord-0994376-63qznrfi authors: Dhillon, R. S.; Rowin, W. A.; Humphries, R. S.; Kevin, K.; Ward, J. D.; Phan, T. D.; Nguyen, L. V.; Wynne, D. D.; Scott, D. A. title: Aerosolisation during tracheal intubation and extubation in an operating theatre setting date: 2020-11-03 journal: Anaesthesia DOI: 10.1111/anae.15301 sha: 76249765ee010d79e6496b1ac78e1624cdd0aab3 doc_id: 994376 cord_uid: 63qznrfi Aerosol‐generating procedures such as tracheal intubation and extubation pose a potential risk to healthcare workers because of the possibility of airborne transmission of infection. Detailed characterisation of aerosol quantities, particle size and generating activities has been undertaken in a number of simulations but not in actual clinical practice. The aim of this study was to determine whether the processes of facemask ventilation, tracheal intubation and extubation generate aerosols in clinical practice, and to characterise any aerosols produced. In this observational study, patients scheduled to undergo elective endonasal pituitary surgery without symptoms of COVID‐19 were recruited. Airway management including tracheal intubation and extubation was performed in a standard positive pressure operating room with aerosols detected using laser‐based particle image velocimetry to detect larger particles, and spectrometry with continuous air sampling to detect smaller particles. A total of 482,960 data points were assessed for complete procedures in three patients. Facemask ventilation, tracheal tube insertion and cuff inflation generated small particles 30–300 times above background noise that remained suspended in airflows and spread from the patient’s facial region throughout the confines of the operating theatre. Safe clinical practice of these procedures should reflect these particle profiles. This adds to data that inform decisions regarding the appropriate precautions to take in a real‐world setting. procedures have been derived from laboratory simulations and cadaveric studies [9] [10] [11] . These have added useful information but do not replicate real-world conditions. Live humans have different tissue, temperature, humidity and excretory characteristics compared with cadaveric and manikin models, which affect the aerosol produced [11] . In addition, operating theatres are positive pressure high airflow environments with multiple air exchanges per hour which affects the behaviour of any suspended small particles. In order to help fill this knowledge gap, a collaboration was established between clinicians, fluid dynamicists and atmospheric scientists. We aimed to determine which stages of tracheal intubation and extubation generated aerosols, and to characterise the count, size, duration and direction of any aerosol produced. Two non-invasive methods that involved no modifications to standard of care were used to detect aerosol: particle image velocimetry, to detect relatively larger particles; and air sampling with spectrometry, to detect relatively smaller particles. In this paper, we define 'aerosol' as particles suspended in air, 'small' particles as Air was sampled at a rate of 6.3 l.min À1 through an inlet positioned 500 mm superior and 500 mm caudal to the patient's nasal aperture (online Supporting Information Appendix S1), which has negligible effect on overall airflow in which the aerosol travelled given that the air exchange of theatre is 5460 l.min À1 . The aerosol sample was transported to the instruments via a single 2500-mm long, 12-mm diameter conductive silicon tube. Other than transportation losses through the tube, the aerosol matrix was not altered. The optical flow diagnostic technique measurement was carried out at three different locationsimmediately superior to the patient's nasal aperture, 300 mm caudal to the nasal aperture and 1100 mm caudal to the nasal aperture (online Supporting Information Appendix S1). Images were pre-processed to increase the signal-to-noise ratio, and the high-intensity peaks in each image used to calculate aerosol counts. For dispersion medium analysis, an in-house particle tracking velocimetry algorithm detected particles with a specified intensity threshold in one frame, then searched for the pair of that particle in subsequent frames. Particles travelling at a faster speed than the recording rate of the low-speed imaging system produced trajectory lines which were used to calculate the landing distance and time. Before inducing general anaesthesia, background noise was evaluated with 12 healthcare workers, three researchers and the patient in the operating theatre, to establish the background noise created by normal theatre traffic (counts 0.09 cm À3 and 60 cm À3 , respectively). Sets of low-speed images were recorded and analysed for particle count, and the count was set as the noise level. Three cases were studied from patient arrival in the operating theatre until transfer to their bed from the operating laryngoscope introduction and throat pack insertion did not produce aerosols above background noise in particle counts from APS (Fig. 2) and MiniWRAS. Facemask ventilation in a patient who had received neuromuscular blockers using oxygen at 6-10 l.min À1 , tracheal tube insertion and cuff inflation (to the point of gas leak elimination) produced mostly small particles < 5 µm in concentrations 30-300 times greater than background noise (p < 0.001) ( Table 1) . These counts were supported by the particle image velocimetry data. sampling methods (Fig. 3) . However, facemask ventilation, throat pack removal and patient coughing, were associated with aerosol production of small particles < 4 µm on both air sampling measurements in concentrations that were significantly greater than baseline measurements (p < 0.001). The mean particle concentrations during intubation and extubation are summarised in Table 2 . The dispersion medium of particles was obtained from the particle image velocimetry system, using twodimensional data and extrapolated to three dimensions. The procedural step that generated the largest particle count was used to calculate dispersion medium, which in Traditional models view aerosols as either small (≤ 5 µm) or large particles (> 5 µm) to understand behaviour [12, 13] . Larger particles travel shorter distances, do not remain airborne for long durations, and settle quickly resulting in surface contamination near the source [14, 15] . Smaller particles, or those that experience a low relative humidity, will shrink in size due to evaporation, resulting in a plume that moves with ambient air currents, remains airborne for longer durations and travels further [14, 16] . Newer aerosol models that add warm and moist microenvironments within a plume sustain small particles for even longer durations and distances [17] . An example of smaller particles is those generated by facemask ventilation. physics principles rather than experimental data [18] . The truth probably lies somewhere in the middle, but further study is required to characterise aerosol clearance times. There is a growing number of studies examining aerosol generation. Ten studies have examined healthcare workers retrospectively for exposure to infectious agents with serology and chest imaging to provide indirect evidence of aerosol production [2] , two have looked at fluorescent dyes in manikins [19, 20] , one looked at the protective effect of intubation shields with simulated aerosols [21] , and another looked at viability of aerosolised SARS-CoV-2 on different surfaces [22] . These methods do not replicate the air temperature, humidity, viscosity, surface tension of mucosal surfaces, air-mucus interface, liquid sheet fragmentation, flow induced particle dispersion or secretory/excretory characteristics of live humans [23] variables affecting the characteristics of the aerosol produced [24] . Furthermore, previous studies do not replicate the clinical environment in terms of number and location of staff, turbulent flows from ducts, air exchange, positive or negative pressure ventilation, temperature or humidityvariables affecting the flow in which the aerosol resides [25] . Understanding background noise is essential in order to differentiate the often-small signals from procedures above ambient changes in an aerosol population, which in an indoor setting can be substantial. Measures were taken to avoid false-positive signals. We established background noise levels in an empty theatre by performing overnight recording, and in an occupied theatre with normal levels of healthcare worker traffic. In addition, staff wore N95 masks to prevent leakage of their own aerosols into the field. Our results also demonstrate that the methods used were sufficiently sensitive to avoid false negatives. We believe that these findings inform clinical practice in several ways. Firstly, measures should be taken to limit unnecessary operating theatre traffic during aerosolgenerating steps, particularly facemask ventilation. Secondly, personal protective equipment needs to be appropriate for the size of particle generated. For example, standard surgical masks lack tight seals and would not prevent the entry of small particles travelling in flows through gaps between mask and face. COVID-19 has changed the way we perform aerosolgenerating procedures. This study provides detailed data on aerosol generation from actual patients in an operating theatre setting. It demonstrates that positive pressure mask ventilation, tracheal intubation and procedures and events following extubation generate small particles in counts several hundred times over baseline, which remain suspended in air and spread throughout theatre. These findings should be used to inform safe anaesthetic practice, and lead to more rational personal protective equipment use. 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Layout of the operating theatre showing the location of ceiling air inlets (wavy lines) and wall air outlets (stripes) with direction of air outflow (arrows).