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Record W3015916584 · doi:10.1101/2020.03.03.20029447

Application of combined genomic and transfer analyses to identify factors mediating regional spread of antibiotic resistant bacterial lineages

2020· preprint· en· W3015916584 on OpenAlexfundno aff
Joyce Wang, Betsy Foxman, Ali Pirani, Zena Lapp, Lona Mody, Evan S. Snitkin

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCenters for Disease Control and PreventionUniversity of Michigan
KeywordsEnterococcus faeciumColonizationAntibioticsMicrobiologyEnterococcus faecalisBiologyPhylogenetic treeMethicillin-resistant Staphylococcus aureusStaphylococcus aureusHorizontal gene transferGeneticsGeneBacteria

Abstract

fetched live from OpenAlex

ABSTRACT Background Patients entering nursing facilities (NFs) are frequently colonized with antibiotic resistant organisms (AROs). To understand the determinants of ARO colonization on NF admission we applied whole-genome sequencing to track the spread of four ARO species across regional NFs and evaluated patient-level characteristics and transfer acute-care hospitals (ACHs) as risk factors for colonization. Methods 584 patients from six NFs were surveyed for methicillin-resistant Staphylococcus aureus ( MRSA), vancomycin-resistant Enterococcus faecalis / faecium (VREfc/VREfm) and ciprofloxacin-resistant Escherichia coli (CipREc) colonization. Genomic analysis was performed to quantify ARO spread between NFs and compared to patient-transfer networks. The association between admission colonization and patient-level variables and recent ACH exposures was examined using multivariable regression models. Results The majority of ARO isolates across study sites belonged to major healthcare-associated lineages: MRSA (ST5;N=89/117); VREfc (ST6;N=68/75); CipREc (ST131; N=58/64), and VREfm (clade A; N=129/129). While the genomic similarity of strains between NF pairs was associated with overlap in their feeder ACHs (Spearman’s rho=0.44-0.75, p<0.05 for MRSA, VREfc and CipREc), limited phylogenetic clustering by either ACH or NF supported regional endemicity. Significant predictors for ARO colonization on NF admission included lower functional status (adjusted odds ratio [aOR]>1 for all four AROs) and recent exposure to glycopeptides (aOR>2 for VREfm, VREfc and MRSA) or 3 rd /4 th -generation cephalosporins (aOR>2 for MRSA and VREfm). Transfer from specific ACHs was an independent risk factor for only one ARO/ACH pair (VREfm/ACH19, aOR=2.48[1.06-5.83]). Conclusion In this region, healthcare-associated ARO lineages are endemic among connected NFs and ACHs, making patient characteristics more informative of NF admission colonization risk than exposure to specific ACHs. Summary Using a combination of whole-genome sequencing, patient transfer and clinical data, we discerned the dissemination of four high-priority antibiotic-resistant organisms (ARO) in the regional healthcare network, and epidemiolocal drivers underlying the high ARO importation rate into regional nursing facilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.351
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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