Quantitative Fit Test of a 3D Printed Frame Fitted Over a Surgical Mask: An Alternative Option to N95 Respirator

Personalized 3D-printed frames to reduce leak from N95 filtering facepiece respirators: A prospective crossover trial in health care workers

Correctly fitting N95 filtering facepiece respirators (FFRs) have become increasingly important in health care throughout the COVID-19 pandemic. We evaluated the hypothesis that personalized 3D-printed frames could improve N95 FFRs quantitative fit test pass rates and test scores in health care workers (HCWs). HCWs were recruited at a tertiary hospital in Adelaide, Australia (ACTRN 12622000388718). A mobile iPhone camera + app was used to produce 3D scans of volunteers' faces, which were then imported into a software program to produce personalized virtual scaffolds suited to each user's face and their unique anatomical features. These virtual scaffolds were printed on a commercially available 3D printer, producing plastic (and then silicone-coated, biocompatible) frames that can be fitted inside existing hospital supply N95 FFR. The primary endpoint was improved pass rates on quantitative fit testing, comparing participants wearing an N95 FFR alone (control 1) with participants wearing the frame + N95 FFR (intervention 1). The secondary endpoint was the fit factor (FF) in these groups, and R-COMFI respirator comfort and tolerability survey scores. N = 66 HCWs were recruited. The use of intervention 1 increased overall fit test pass rates to 62/66 (93.8%), compared to 27/66 (40.9%) for controls. (OR for pFF pass 20.89 (95%CI: 6.77, 64.48, p < 0.001.) Average FF increased, with the use of intervention 1-179.0 (95%CI: 164.3,193.7), compared to 85.2 (95%CI: 70.4,100.0) with control 1. Pass rates and FF were improved with intervention 1 compared to control 1 for all stages of the fit-test: bending, talking, side-to-side, and up-down motion. (p < 0.001 all stages). Tolerability and comfort of the frame were evaluated with the validated R-COMFI respirator comfort score, showing improvement with the frame compared to N95 FFR alone (p = 0.006). Personalized 3D-printed face frames decrease leakage, improve fit testing pass rates and FF, and provide improved comfort compared to the N95 FFR alone. Personalized 3D-printed face frames represent a rapidly scalable new technology to decrease FFR leakage in HCW and potentially the wider population.

A critical review on the role of leakages in the facemask protection against SARS-CoV-2 infection with consideration of vaccination and virus variants

The protection provided by facemasks has been extensively investigated since the beginning of the SARS-CoV-2 outbreak, focusing mostly on the filtration efficiency of filter media for filtering face pieces (FFP), surgical masks, and cloth masks. However, faceseal leakage is a major contributor to the number of potentially infectious airborne droplets entering the respiratory system of a susceptible individual. The identification of leaking spots and the quantification of leaking flows are crucial to estimate the protection provided by facemasks. This study presents a critical review on the measurement and calculation of facemask leakages and a quantitative analysis of their role in the risk of SARS-CoV-2 infection. It shows that the pairing between the mask dimensions and the wearer's face is essential to improve protection efficiency, especially for FFP2 masks, and summarizes the most common leaking spots at the interface between the mask and the wearer's face. Leakage is a crucial factor in the calculation of the protection provided by facemasks and outweighs the filtration performances. The fit factors measured among mask users were summarized for different types of face protection. The reviewed data were integrated into a computational model to compare the mitigation impact of facemasks with vaccination with consideration of new variants of SARS-CoV-2. Combining a high adoption rate of facemasks and a high vaccination rate is crucial to efficiently control the spread of highly infectious variants.