918 In this study, we sought to identify whether increased sample
surface area was associated with increased detection of C. difficile from environmental samples collected from patient rooms in a tertiary-care hospital in a nonoutbreak setting. We explored whether this relationship existed for both floor and bedrail samples, and across several different microbiologic methods, including qPCR and enrichment culture.
Methods Design and setting
We used an efficient split-plot sampling design14,15 to assess the impact of sample surface area on C. difficile detection from hospital environmental samples while controlling for room-level clustering of bacterial burden. Small surface-area and large surface-area pairs of bedrail and floor environmental samples were selected from 12 rooms (N=48) in 3 inpatient units (2 general medicine, 1 intensive care), over the course of 2 days in September 2017 in a large tertiary-care hospital located in Tor- onto, Canada.
Sample collection
For sampling, patient rooms were selected based on a categor- ization of risk of contamination: high-risk rooms were defined as those of patients with active C. difficile diarrhea identified by hospital infection control staff; medium-risk rooms were those of patients with a history of in-hospital antibiotic use in the last 14 days; and low-risk rooms were those of patients without a history of C. difficile or in-hospital antibiotic use in the previous 14 days. All high-risk rooms available in the hospital were selected (N=5), as well as a selection of medium-risk (N=2) and low-risk rooms (N=5) on the same wards. All samples were collected independently of ward- and room-cleaning schedules, and the timing of most recent cleaning was not known or recorded. The hospital environmental cleaning policy specified daily routine cleaning and terminal cleaning as surface cleaning with quaternary ammonium. Clostridium difficile cleaning was surface cleaning with accelerated hydrogen peroxide performed twice daily.16 In each selected room, 2 pairs of samples were collected using
sterile cellulose sponges premoistened in Dey-Engley neutralizing buffer (Scigiene, Scarborough, ON). The first pair consisted of a small surface-area floor sample (32×32cm, 0.10m2) and a large surface-area floor sample (100×100 cm, 1m2). These samples were collected from contiguous spaces alongside the patient bed on the side of usual access. The second pair consisted of a small surface- area bedrail sample (7.7×30 cm, 0.023m2) and a large surface-area bedrail sample (77×30cm, 0.23m2). These samples extended down the side of the bedrail panel. Again, these samples were contiguous and from the bedrail on the side of usual access. Small surface-area samples were taken from the portion of the bedrail corresponding to the head of the bed, while large surface-area samples included the remainder of the bedrail. Surfaces were wiped using perpendicular, overlapping “S” patterns using a defined protocol.17
Outcome measurements
Clostridium difficile cells were extracted from sponge swabs. Briefly, 40mL sterile double-distilled water was added to the sample bag, which was then vigorously massaged between fingers for 90 seconds. Extracted cells and environmental debris were
Kevin Antoine Brown et al
collected from the liquid by centrifugation at 7,500×g for 15 minutes. All but 1mL of the supernatant was removed, and the pellet was resuspended and transferred to a microcentrifuge tube, which was centrifuged at 10,000×g for 5 minutes. The entire supernatant was removed, and the pellet was resuspended in 500 µL of 20% Dey-Engley neutralizing broth (Scigiene) diluted in sterile water. With half of the sample, DNA was extracted using the
ZymoBIOMICS DNA Miniprep Kit (Zymo Research, Irvine, CA) using a 1-hour bead-beating step and a DNA elution volume of 50 µL. We conducted quantitative polymerase chain reaction (qPCR) on 2 targets. A 157-bp conserved region of the C. difficile 16S rRNA gene (present in ~10 copies per genome) and an internal TaqMan probe (Applied Biosystems, Foster City, CA) were used for detection of all C. difficile strains.18 A 127-bp region of the toxin B gene (present in 1 copy per genome) and a corre- sponding TaqMan probe were used to specifically detect toxigenic C. difficile strains.19 Reactions (10 µL) containing 5 µL JumpStart Taq ReadyMix for qPCR (D7440, Sigma-Aldrich, St Louis, MO), 0.1 µL reference dye for qPCR (R4526, Sigma), 0.4 µL 25mM MgCl2,0.5µM(each) forward and reverse primers, 0.15µMTaqMan probe, and 3.85µL DNA were prepared. These were run in triplicate on an ABI 7900HT thermocycler (Applied Biosystems) under the following conditions: 50°C for 2 minutes, 95°C for 10 minutes, 45 cycles at 95°C for 15 seconds, 60°C for 1 minute. The other half of the sample was enriched and cultured using banana broth (Hardy Diagnostics, Santa Maria, CA).19 Samples positive by enrichment culture were ribotyped (Appendix 1). We coded 5 outcome variables. First, 16S qPCR positivity and
toxin B qPCR positivity were coded such that any threshold cycle (Ct)<45 was considered positive, and any Ct ≥ 45 (or unde- termined) was considered negative. The enrichment cultured- based measure was coded as either positive or negative. Second, we converted qPCR Ct to spore counts for 16S qPCR and for toxin B qPCR based on their respective standard curves. These C. difficile standard curves were generated by serial dilution of a spore stock quantitated using a hemocytometer. As is customary in occupational exposure assessment analyses, we truncated the estimated spore count distribution at 0.5 spores, corresponding to half the detection limit.20
Covariates
We coded risk-factor variables corresponding to sample type (bedrail or floor) and room risk of contamination (low, medium, and high). Sample surface area was encoded as a categorical variable (small or large). To adjust for surface area in our mul- tivariable21 models, we also encoded sample surface area in as a continuous, log10-transformed variable, log10 (m2). Finally, we also coded 3 clustering variables: sample pair identifier, room identifier, and ward identifier.
Statistical analyses
For descriptive analyses, we measured the proportion of samples that were positive for the binary outcome variables and the geo- metric (log10) mean spore count for the continuous outcome variables across the 3 risk-factor variables. For multivariable analyses, we used separate logistic mixed
effects models for the presence versus absence of C. difficile by 16S qPCR, toxin B qPCR, and enrichment culture. We used negative binomial mixed effects models for the estimated spore count by
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