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Fig. 1. Bacterial contamination, species richness, and α diversity of stethoscopes in the medical intensive care unit (ICU). Stethoscopes were analyzed for level of total bacterial contamination as quantified by 16S rRNA gene amplification (A). The nature of the communities were assessed by species richness, reflecting the number of different bacterial taxa identified (B), and α (within-community) diversity was calculated using the Shannon index, an indicator that encompasses both richness and evenness of distribution among the identified taxa (C).
Prac.St, practitioner personal stethoscopes;
PtRm.St, patient-room single-use stethoscopes;
Cln.St, clean single-use stethoscopes; Backg, clean swabs that serve as controls for background bacterial DNA originating from swabs, saline, reagents or anywhere along the processing and sequencing pipeline. *P<.05 (Wilcoxon rank-sum test).
concentrations than the clean stethoscopes (P=1.8×10−5; Wilcoxon rank-sum test). Both practitioner and patient-room stethoscopes were significantly higher in 16S quantity than back- ground controls, whereas clean stethoscopes were indistinguishable from the background controls (P=.967; Wilcoxon rank-sum test). We analyzed species richness, which reflects the number of dif-
ferent taxa within each community (Fig. 1(B)). Practitioner stetho- scopes had significantly greater richness than either patient-room or clean stethoscopes (P=.003 and P=.004, respectively; pairwise Wil- coxon rank-sum test). All stethoscopegroupsweresignificantly higher in richness than the background controls. There was no significant difference in richness between patient-room and clean stethoscopes. We then assessed α diversity of the stethoscope bacterial
communities using the Shannon diversity index, ametricthat incorporates both richness and evenness of distribution, with higher diversity reflecting greater richness and more even distribution of taxa (Fig. 1(C)). Practitioner, patient-room, and clean stethoscopes were all significantly more diverse than the background controls (P=.0005, P=.003, and P=.0004, respectively; pairwise Wilcox rank-sum test). In contrast, there was no significant difference in Shannon diversity between stethoscope groups. Taxa identified on practitioner stetho- scopes at>1% relative abundance within their respective communities are shown in a heatmap in Fig. S1. To compare the overall bacterial communities of the different
groups, we calculated UniFrac distances among samples using unweighted and weighted methods, then plotted them on prin- cipal coordinates analysis (PCoA) plots (Fig. 2). The UniFrac metric compares complex microbial communities based on the phylogenetic relatedness of the bacteria contained within the communities, and the PCoA plot provides an overview visuali- zation of community relatedness. Using the unweighted UniFrac metric (Fig. 2(A)), which takes
into account the presence or absence of taxa in different samples but not their relative abundance, bacterial communities were significantly different between all stethoscope groups (P<.001; PERMANOVA). Using the weighted UniFrac metric (Fig. 2(B)), which takes into account the presence or absence as well as relative abundance of bacteria comprising the communities, we observed that practitioner and patient-room stethoscopes differed
from the clean stethoscopes and background controls (P<.001; PERMANOVA), but the practitioner and patient-room stetho- scopes were not significantly different from one another (P=.106; PERMANOVA), nor were clean stethoscope and background controls (P=.07; PERMANOVA). These results suggest that both types of in-use stethoscopes differ substantially from the 2 types of control samples (clean stethoscopes and background), and that low-abundance taxa were mainly responsible for the differences between practitioner and patient-room stethoscopes, and for the differences between clean stethoscopes and background controls. We next sought to determine which genera were responsible for
the differences between bacterial communities on the in-use stetho- scopes and the 2 types of controls on the weighted UniFrac PCoA. Figure 3 shows the top taxa responsible for separation of the com- munities plotted as vectors on the weighted PCoA, thus indicating which bacteria are responsible for differentiating communities located in distinct regions of the PCoA. The genera Methylobacterium, Pseudomonas, and Acinetobacter drove the separation of the clean stethoscopes and background controls from the practitioner and patient-room stethoscopes, indicating that these taxa are mainly derived from background sources in this sample set. Conversely, the practitioner and patient-room samples were characterized by Por- phyromonas, Bacteroides, Granulicatella, Actinomyces, Prevotella, Streptococcus, Staphylococcus, Corynebacterium, and Propionibacter- ium, which are common oral, skin, and gut bacteria.
Effects of cleaning on stethoscope bacterial biomass and communities
Although patient-room stethoscopes are typically used only for a single patient, practitioner stethoscopes are used on multiple patients, raising the possibility of microbial transfer. Because prac- titioners may clean their stethoscopes between uses, we analyzed the effect of cleaning on the bacterial biomass on stethoscope dia- phragms based on levels of 16S rRNA DNA. For 1 set of stetho- scopes (set B; n=10), we used a standardized cleaning method: vigorously wiping the diaphragm with a hydrogen-peroxide wipe for 60 seconds. For another set (set C; n=20), we asked each practi- tioner to clean the diaphragm themselves with the method they
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