Effectiveness of Aerosol Filtration by Mechanical Filter, Electrostatic Filter, and Electrostatic Filter with Heat Moisture Exchanger in Healthy Volunteers Nebulized with Normal Saline: A Randomized Crossover Trial

Background: The COVID-19 outbreak in Thailand has raised concerns that aerosol generated from the nebulizer might be contaminated with SARS-CoV-2. To prevent this, various types of viral filters might be useful in decreasing the spread of COVID-19. Objective: To compare the effectiveness of mechanical filter, electrostatic filter, and electrostatic filter with heat moisture exchanger (HME) to no filter in filtration nebulized normal saline solution (NSS). Materials and Methods: The present study was a randomized, single-blind crossover trial. Forty-eight healthy volunteers were given nebulized NSS via the mouthpiece. At the end of the system, a filter or no filter was attached according to the randomization series. The primary outcome was the number of particles per cubic foot measured at two locations, distal to the filter, which is referred to as ‘near distant’ and 180 centimeters away from the system, which is referred to as ‘far distant’ during nebulization. The measurement was recorded every minute from the beginning of nebulization until the fifth minute. Results: Forty-eight healthy volunteers were studied with four types of filtration methods. The number of particles per cubic foot was compared by intervention using a multi-level mixed effect linear regression, a statistical method that accounted for the repeated measurements within each participant. The highest number of particles was measured at the near distant in no filter group, with a mean of 188,034 particles per cubic feet and a maximum of 370,816 particles per cubic feet and lowest number was measured at the far distant in mechanical filter group with a mean of 199 per cubic feet and a maximum of 316 particles per cubic feet. The ranking of the number of particles from the highest to the lowest were no filter, electrostatic filter, electrostatic filter with HME, and mechanical filter. Each type of filter significantly decreased the number of particles compared to no filter usage (p<0.001). However, no significant difference was found among each type of filter. Conclusion: Mechanical filter, electrostatic filter, and electrostatic with HME lessened the number of particle dispersion into the environment compared with no filter attached to the nebulization equipment.

Publication Details

Journal
Journal of the Medical Association of Thailand
Published
2024-11-19
DOI
https://doi.org/10.35755/jmedassocthai.2024.11.888-893-01340
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effectiveness of Aerosol Filtration by Mechanical Filter, Electrostatic Filter, and Electrostatic Filter with Heat Moisture Exchanger in Healthy Volunteers Nebulized with Normal Saline: A Randomized Crossover Trial

Journal of the Medical Association of Thailand
COVID-19 diagnosis using AI
article

Effectiveness of Aerosol Filtration by Mechanical Filter, Electrostatic Filter, and Electrostatic Filter with Heat Moisture Exchanger in Healthy Volunteers Nebulized with Normal Saline: A Randomized Crossover Trial

article en

Abstract

Background: The COVID-19 outbreak in Thailand has raised concerns that aerosol generated from the nebulizer might be contaminated with SARS-CoV-2. To prevent this, various types of viral filters might be useful in decreasing the spread of COVID-19. Objective: To compare the effectiveness of mechanical filter, electrostatic filter, and electrostatic filter with heat moisture exchanger (HME) to no filter in filtration nebulized normal saline solution (NSS). Materials and Methods: The present study was a randomized, single-blind crossover trial. Forty-eight healthy volunteers were given nebulized NSS via the mouthpiece. At the end of the system, a filter or no filter was attached according to the randomization series. The primary outcome was the number of particles per cubic foot measured at two locations, distal to the filter, which is referred to as ‘near distant’ and 180 centimeters away from the system, which is referred to as ‘far distant’ during nebulization. The measurement was recorded every minute from the beginning of nebulization until the fifth minute. Results: Forty-eight healthy volunteers were studied with four types of filtration methods. The number of particles per cubic foot was compared by intervention using a multi-level mixed effect linear regression, a statistical method that accounted for the repeated measurements within each participant. The highest number of particles was measured at the near distant in no filter group, with a mean of 188,034 particles per cubic feet and a maximum of 370,816 particles per cubic feet and lowest number was measured at the far distant in mechanical filter group with a mean of 199 per cubic feet and a maximum of 316 particles per cubic feet. The ranking of the number of particles from the highest to the lowest were no filter, electrostatic filter, electrostatic filter with HME, and mechanical filter. Each type of filter significantly decreased the number of particles compared to no filter usage (p<0.001). However, no significant difference was found among each type of filter. Conclusion: Mechanical filter, electrostatic filter, and electrostatic with HME lessened the number of particle dispersion into the environment compared with no filter attached to the nebulization equipment.

Journal of the Medical Association of ThailandVol. 107(11)
Mahidol University
Openalex Percentile: Top 65%
COVID-19 diagnosis using AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.