June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Aerosolization Detection with Noncontact Tonometry in Corneal Phantoms using an Air Quality Indicator
Author Affiliations & Notes
  • Jonathan Thomas Ibinson
    Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
  • Atieh Yousefi Koupaei
    The Ohio State University, Columbus, Ohio, United States
  • Cynthia J Roberts
    Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
    Ophthalmology, The Ohio State University, Columbus, Ohio, United States
  • Matthew Aaron Reilly
    Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
    Ophthalmology, The Ohio State University, Columbus, Ohio, United States
  • Footnotes
    Commercial Relationships   Jonathan Ibinson, None; Atieh Yousefi Koupaei, None; Cynthia Roberts, Oculus Opitgerate GmbH (C), Ziemer Ophthalmic Systems AG (C); Matthew Reilly, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2569. doi:
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      Jonathan Thomas Ibinson, Atieh Yousefi Koupaei, Cynthia J Roberts, Matthew Aaron Reilly; Aerosolization Detection with Noncontact Tonometry in Corneal Phantoms using an Air Quality Indicator. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2569.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To evaluate the performance of a static air quality monitor to measure aerosolization of a dynamic air puff from a noncontact tonometer (NCT) using cornea phantoms.

Methods : Three rubber corneal phantoms of different stiffnesses were used to represent intraocular pressure (IOP) values of 6, 13, and 43 mmHg. No liquid components and therefore no aerosol-generating potential was present. Reported concentrations of particulate matter (PM) less than 2.5 and 10µm, respectively PM2.5 and PM10, were recorded using a static air quality indicator (AQI; LKC 1000S+) during an air puff generated using an Ocular Response Analyzer G3.The phantoms were mounted in a styrofoam head with eye socket locations. The AQI was set in four locations around the phantom. Depending on indicator position with respect to the phantom, the locations were defined as “upper/lower” and “same/opposite.” Tests were performed in an isolated room. Baseline AQI values were recorded prior to each test with a one-minute delay between tests to allow the device to recalibrate. An ANOVA was performed to assess the effect of covariates IOP and location on changes to AQI measurements from the baseline, ΔPM2.5 and ΔPM10. A Monte Carlo simulation with 10,000 replicates was undertaken to determine the likelihood of observing published trends by chance. The statistical significance threshold was p<0.05.

Results : No correlations were found between PM2.5 and IOP (p=0.3943) or location (p=0.3049). Reported concentrations of PM10 depended significantly on both IOP (p=0.0241) and location (p=0.0167), specifically the lower opposite location (p=0.0031). Monte Carlo simulations using these results suggest that the likelihood of finding a spurious positive correlation between IOP and PM at the upper same location are 53% and 92% for PM2.5 and PM10, respectively.

Conclusions : While a statistically significant correlation was found between reported PM10 and IOP/location, even the highest reported PM values were far below the AQI sensitivity of ±10 and ±15 µg/m3 for PM2.5 and PM10, respectively. The AQI updates every 3 seconds, yet the NCT puff is approximately 30 ms in duration. Since these experiments were conducted in the absence of any aerosol source (i.e., tear film), published IOP-PM correlations are likely a result of air pressure magnitude. (Tang 2020) Therefore, the AQI is not capable of quantifying aerosolization with an NCT air puff.

This is a 2021 ARVO Annual Meeting abstract.

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