Assessment of a novel continuous cleaning device using metatranscriptomics in diverse hospital environments

Abstract

Introduction: Despite routine implementation of cleaning and disinfection practices in clinical healthcare settings, high-touch environmental surfaces and contaminated equipment often serve as reservoirs for the transmission of pathogens associated with healthcare-associated infections (HAIs).

Methods: The current study involved the analysis of high-touch surface swabs using a metatranscriptomic sequencing workflow (CSI-Dx™) to assess the efficacy of cleanSURFACES® technology in decreasing microbial burden by limiting re-contamination. This is a non-human single center study conducted in the Emergency Department (ED) and on an inpatient Oncology Ward of Walter Reed National Military Medical Center that have followed hygienic practices during the COVID-19 pandemic environment.

Results: Although there was no difference in observed microbial richness (two-tailed Wilcoxon test with Holm correction, P > 0.05), beta diversity findings identified shifts in microbial community structure between surfaces from baseline and post-intervention timepoints (Day 1, Day 7, Day 14, and Day 28). Biomarker and regression analyses identified significant reductions in annotated transcripts for various clinically relevant microorganisms' post-intervention, coagulase-negative staphylococci and Malassezia restricta, at ED and Oncology ward, respectively. Additionally, post-intervention samples predominantly consisted of Proteobacteria and to a lesser extent skin commensals and endogenous environmental microorganisms in both departments.

Discussion: Findings support the value of cleanSURFACES®, when coupled with routine disinfection practices, to effectively impact on the composition of active microbial communities found on high-touch surfaces in two different patient care areas of the hospital (one outpatient and one inpatient) with unique demands and patient-centered practices.

Keywords: HAI; continuous cleaning; emergency department; metatranscriptomics; oncology ward.

Figure 1

Observed microbial richness for emergency and oncology ward before (baseline) and after AIONX® intervention (Day 1, Day 7, Day 14, and Day 28). While not statistically significant, there was an observed decrease in microbial richness from baseline to Day 1 and 7 samples.

Figure 2

Sample networks of surface swabs connect samples with similar microbial community compositions (weighted jaccard distance < 0.4) when colored by pre/post AIONX® intervention and sampling day reveal successional changes in the microbial community over time. A total of 26 isolated vertices represented samples with < 60% taxa in common with any other samples have been omitted from the network.

Figure 3

Comparisons of baseline samples to post AIONX® intervention across emergency department and oncology ward. Binary Jaccard distances were partitioned into nestedness (species loss) and turnover (species gain) to explain their contribution to changes compared to baseline. Higher Jaccard distances indicate more dissimilarity in the microbial community where as lower Jaccard distances indicate more similarity in the microbial community. Turnover contributes more to Jaccard distances as compared to nestedness. Nestedness species loss stays relatively stable over time while turnover (species gain) increases over time.

Figure 4

Proteobacteria from the orders Pseudomonadales and vibrionales vary over time in the emergency department (A) and oncology ward (B). Counts are normalized to 5,000 microbial reads per sample and are colored by their most specific taxonomic classification.

Figure 5

Lefse pre/post for emergency department (A) and oncology ward (B). Taxa that were enriched in Pre/baseline are indicated with red bars, while taxa that were enriched in the post-intervention samples are indicated with blue bars. Significant (Kruskal-Wallis, p ≤ 0.05 and log (LDA) ≥ 1.5) features are shown.

Figure 6

The normalized counts of clinically relevant taxa associated with HAIs are shown before and after AIONX® intervention. A linear regression shows counts per 5 k microbial reads depend on the square root of days post-intervention for each taxon and building, with a standard error of 95% shown in gray.