Short-duration selective decontamination of the digestive tract infection control does not contribute to increased antimicrobial resistance burden in a pilot cluster randomised trial (the ARCTIC Study)

Abstract

Objective: Selective decontamination of the digestive tract (SDD) is a well-studied but hotly contested medical intervention of enhanced infection control. Here, we aim to characterise the changes to the microbiome and antimicrobial resistance (AMR) gene profiles in critically ill children treated with SDD-enhanced infection control compared with conventional infection control.

Design: We conducted shotgun metagenomic microbiome and resistome analysis on serial oropharyngeal and faecal samples collected from critically ill, mechanically ventilated patients in a pilot multicentre cluster randomised trial of SDD. The microbiome and AMR profiles were compared for longitudinal and intergroup changes. Of consented patients, faecal microbiome baseline samples were obtained in 89 critically ill children. Additionally, samples collected during and after critical illness were collected in 17 children treated with SDD-enhanced infection control and 19 children who received standard care.

Results: SDD affected the alpha and beta diversity of critically ill children to a greater degree than standard care. At cessation of treatment, the microbiome of SDD patients was dominated by Actinomycetota, specifically Bifidobacterium, at the end of mechanical ventilation. Altered gut microbiota was evident in a subset of SDD-treated children who returned late longitudinal samples compared with children receiving standard care. Clinically relevant AMR gene burden was unaffected by the administration of SDD-enhanced infection control compared with standard care. SDD did not affect the composition of the oral microbiome compared with standard treatment.

Conclusion: Short interventions of SDD caused a shift in the microbiome but not of the AMR gene pool in critically ill children at the end mechanical ventilation, compared with standard antimicrobial therapy.

Keywords: ANTIBIOTIC THERAPY; DRUG RESISTANCE.

Figure 1

PICnIC AMR and microbiome monitoring substudy enrolment schema. Children became eligible for the substudy upon enrolment to the PICnIC Study. Inclusion into PICnIC is outlined by Brown et al and Pathan et al. Briefly, children arriving in the PICU who required mechanical ventilation were assessed by clinicians, and patients judged to require greater than 48 hours of invasive respiratory support became eligible. Patients were not eligible if they were known to be allergic to colistin, tobramycin or nystatin, or if they were known to be colonised by a microorganism resistant to any of the three previous drugs. Once enrolled to PICnIC, children became eligible for this substudy. This substudy used a deferred consent model for collecting research samples. Research nurses were instructed to collect pretreatment samples as soon as clinically feasible. Oropharyngeal samples were to be taken before the application of SDD paste in the case of treatment patients and as soon as possible for standard care patients. Rectal swabs were to be taken as soon as possible but within 6 hours of admission for all patients. Six hours was chosen as the transit time of the non-absorbable SDD drugs from the stomach to rectum should not have occurred within this window. Parents/guardians were approached for consent as soon as was reasonable. Samples collected from children with no deferred consent were destroyed. Patients who were extubated and stopped SDD prior to consent being collected were excluded from the trial. If consent was received, treatment samples were advised to be collected as soon as a clinical decision was made to end mechanical ventilation. This time point was chosen as collection of samples from sedated children would impart the least amount of discomfort. The substudy required that both pairs of samples be collected. Any child who was missing one or more of the first pairs of samples was excluded from the study. Recovery samples were scheduled for 2–3 months post-SDD cessation. For recovery samples, parents were asked to collect an oral swab in place of the oropharyngeal swab, because it can be easily performed by non-specialists on small children. Parents were also asked to collect a faecal sample using the OMNIGene Gut tube from DNAGenotek. For children who continued to stay in the PICU after extubation, nurses collected OP and RS, unless faeces were passed. Collection of recovery samples was not an exclusion criterion. Boxes with dashed outlines indicate inclusion/exclusion points. The box with diagonal lines indicates the minimum requirements for inclusion to this substudy. AMR, antimicrobial resistance; ASAP, as soon as possible; OP, oropharyngeal; PICnIC, Paediatric Intensive Care and Infection Control; PICU, paediatric intensive care unit; RS, rectal swab; SDD, selective decontamination of the digestive tract.

Figure 2

Microbiome diversity and AMR gene pool of critically ill children. Critically ill children admitted to the Cambridge University Hospital paediatric intensive care unit were sampled to identify the composition of their microbiome and resistome. Healthy control children were selected from the NIH RESONANCE trial conducted in the USA. (A) Microbiome alpha diversity (Shannon’s Index) is comparable between admission samples of critically ill children and healthy control children. Sequences were classified using Kraken2 with a confidence level of 0.1 and the 05/2023 nt library. (B) Chao1 index of alpha diversity. (C) The microbial composition of critically ill children is divergent from age-matched healthy control children. Critically ill children have increased proportions of opportunistic pathogens and lower proportions of commensal Gram-negative anaerobes. We selected the top 10 most abundant bacteria from healthy controls and the top 10 most abundant bacteria in critically ill children not represented in list one, grouping all other microbiota into ‘other’. The median proportion of each genus is represented in the stacked bar chart. (D) Beta diversity indicates that the composition of both groups of children are divergent, with a statistically separate centroid clustering (PERMANOVA p=0.001, ellipse level set to 60%). Orange triangles represent critically ill patients, and blue circles healthy control samples. (E) MaAsLin2 differential representation of selected opportunistic pathogens responsible for VAP, representative secondary fermenters of the lower GI tract and significantly different intrinsically colistin-resistant bacteria. (F) nMDS plotting of Bray-Curtis distances of AMR genes measured as reads per kilobase of gene per megabase of sequencing (RPKM) and identified by ARIBA analysis. (G) Differently enriched AMR gene classes identified by MaAsLin2 between healthy controls and critically ill children. (H) AMR gene burden measured as RPKM for AMR compound structural classes and generalised function. Genes were pooled by target antimicrobial drug class. (I) Total RPKM between groups. AMR, antimicrobial resistance; MLS, macrolide, lincosamide and streptogramin B; NIH, National Institutes of Health; nMDS, non-metric dimensional scaling; PERMANOVA, permutational analysis of variance; VAP, ventilator-associated pneumonia.

Figure 3

Changes in the lower GI microbiome during SDD-enhanced infection control in critically ill children. (A) Alpha diversity of patients calculated at species level using Shannon’s Index. No significant difference was observed after correcting for multiple comparisons. (B) Chao1 index of microbial richness. No significant difference was observed after correcting for multiple comparisons. (C) nMDS plot of beta diversity clustering of faecal microbiomes based on Bray-Curtis distances. No significant difference was observed in the clustering of any group after multiple corrections. Diamond=SDD admission, inverted triangle=SDD extubation, asterisk=SDD recovery, circle=SC admission, square=SC extubation, triangle=SC recovery. (D) Composition of the lower GI microbiome of patients at admission compared with healthy controls. The 10 most abundant families of microbiota by median proportion in the GI microbiome of standard care admission patients, and the 10 most abundant microbiota families in SDD admission patients by median proportion not represented in the first list were selected with the remaining taxa combined as ‘other’. (E) Lower GI composition of patients at extubation. Taxa are identified as in D. (F) Microbiome composition as median proportions at recovery. Taxa identified as in D. nMDS, non-metric dimensional scaling; SC, standard care; SDD, selective decontamination of the digestive tract.

Figure 4

Changes in the oral microbiome during SDD-enhanced infection control in critically ill children. (A) Alpha diversity in SC patients measured as Shannon’s Index at species level at admission, extubation and recovery 2–3 months post-admission. (B) Chao1 index of microbial richness. (C) Microbiome beta diversity of PICU patients receiving SC. No statistical difference was calculated by PERMANOVA. Diamond=SDD admission, inverted triangle=SDD extubation, asterisk=SDD recovery, circle=SC admission, square=SC extubation, triangle=SC recovery. (D) Composition of the oral microbiome of patients at admission compared with healthy controls. The 10 most abundant families of microbiota by median proportion in the oropharynx of SDD patients at admission, and the 10 most abundant microbiota families in SC admission patients by median proportion not represented in the first list were selected with the remaining taxa combined as ‘other’. (E) Oral microbiome composition of patients and controls at extubation. Taxa are identified as in D. (F) Microbiome composition as median proportions at recovery. Taxa identified as in D. MDS, metric dimensional scaling; PERMANOVA, permutational analysis of variance; PICU, paediatric intensive care unit; SC, standard care; SDD, selective decontamination of the digestive tract.

Figure 5

AMR gene proportion and composition from faecal and oral samples. AMR genes were assembled by ARIBA to the MEGARes V.3.0 library and normalised as reads per kilobase per megabase of sequencing (RPKM). (A) Total AMR gene burden in the lower GI microbiome. The sum of all AMR gene RPKM was compared across all groups. (B) RPKM of each AMR class detected in the faecal microbiome of individuals with SC. (C) RPKM of each AMR class detected in the faecal microbiome of individuals with SDD-enhanced infection control. (D) nMDS of AMR genes in faecal samples identified by ARIBA using the CARD V.3.2.7 database. Diamond=SDD admission, inverted triangle=SDD extubation, asterisk=SDD recovery, circle=SC admission, square=SC extubation, triangle=SC recovery. (E) Total AMR gene burden in the oropharyngeal microbiome. The sum of all AMR gene RPKM was compared across all groups. (F) RPKM of each AMR class detected in the oropharyngeal microbiome of individuals with SC. (G) RPKM of each AMR class detected in the oropharyngeal microbiome of individuals with SDD-enhanced infection control. (H) nMDS of AMR genes in oral samples identified by ARIBA using the CARD V.3.2.7 database. Diamond=SDD admission, inverted triangle=SDD extubation, asterisk=SDD recovery, circle=SC admission, square=SC extubation, triangle=SC recovery. AMR, antimicrobial resistance; MLS, macrolide, lincosamide and streptogramin B; nMDS, non-metric dimensional scaling; SC, standard care; SDD, selective decontamination of the digestive tract.