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. 2022 Aug 1:4:963541.
doi: 10.3389/fmedt.2022.963541. eCollection 2022.

Personalised 3D printed respirators for healthcare workers during the COVID-19 pandemic

Affiliations

Personalised 3D printed respirators for healthcare workers during the COVID-19 pandemic

Aidan D Roche et al. Front Med Technol. .

Abstract

Widespread issues in respirator availability and fit have been rendered acutely apparent by the COVID-19 pandemic. This study sought to determine whether personalized 3D printed respirators provide adequate filtration and function for healthcare workers through a Randomized Controlled Trial (RCT). Fifty healthcare workers recruited within NHS Lothian, Scotland, underwent 3D facial scanning or 3D photographic reconstruction to produce 3D printed personalized respirators. The primary outcome measure was quantitative fit-testing to FFP3 standard. Secondary measures included respirator comfort, wearing experience, and function instrument (R-COMFI) for tolerability, Modified Rhyme Test (MRT) for intelligibility, and viral decontamination on respirator material. Of the 50 participants, 44 passed the fit test with the customized respirator, not significantly different from the 38 with the control (p = 0.21). The customized respirator had significantly improved comfort over the control respirator in both simulated clinical conditions (p < 0.0001) and during longer wear (p < 0.0001). For speech intelligibility, both respirators performed equally. Standard NHS decontamination agents were able to eradicate 99.9% of viral infectivity from the 3D printed plastics tested. Personalized 3D printed respirators performed to the same level as control disposable FFP3 respirators, with clear communication and with increased comfort, wearing experience, and function. The materials used were easily decontaminated of viral infectivity and would be applicable for sustainable and reusable respirators.

Keywords: 3D printing; COVID-19; PPE; facemask; fit-testing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The process of converting an individuals facial scan to a personalized 3D mask is demonstrated. (A) Is a 3D facial CAD Model generated using the Crisalix application while (B) is a 3D facial CAD Model generated from the Artec Eva scanning process. Both models are shown prior to any manipulation. (C,D) The full mask on a participant's virtual face. (E) The final 3DPPE mask worn by the participant.
Figure 2
Figure 2
A comparison of the average scores by exercise for the Alpha Solway 3030V and 3DPPE Fit Testing. The maximum possible fit factor was 200, with a minimum of 100 required to pass. Error bars indicate 95% confidence intervals.
Figure 3
Figure 3
Representation of location of the fit test fail points of each the Alpha Solway 3030V and the 3DPPE masks in comparison to the participants NIOSH Bivariate Panel value.
Figure 4
Figure 4
Comparison of R-COMFI scores. (A) Overall R-COMFI scores for personalized vs. control respirators following simulated clinical scenario and extended at home wear. (B) Comparison of discomfort subsection scores. (C) Comparison of wearing experience subs subsection scores. (D) Comparison of function subsection scores. (E) Sex differences in R-COMFI score for personalized vs. control respirators. Error bars represent 95% confidence interval. P < 0.05 are denoted by “*,” <0.01 by “**,” and <0.0001 by “****.”

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