Genomic Epidemiology of Carbapenemase-Producing Enterobacterales (CPE) in Toronto, Canada
Bibliographic record
Abstract
Objective: We investigated whether whole-genome sequencing (WGS) data altered interpretations of clonal transmission as determined by conventional epidemiology and pulsed-field gel electrophoresis (PFGE) at a tertiary-care hospital (hospital Z, HZ). Methods: We included all carbapenemase-producing Enterobacterales (CPE)–colonized or –infected patients identified via population-based surveillance from 2007 through 2018, who were admitted to HZ during and/or in the year prior to or following CPE detection. HZ reported clonal transmission clusters using epidemiology and PFGE for CPE identified at HZ or reported to HZ by other hospitals as potentially acquired at HZ. We assessed single-nucleotide polymorphism (SNP) phylogenies and case epidemiology. Results: Overall, 85 CPE-colonized or -infected patients were included: 50 were detected at HZ and 35 were detected at another local hospital but were admitted to HZ in the previous or following year. HZ reported 6 transmission clusters (Table 1). SNP analyses confirmed clusters B, C, E, and F. In cluster A, SNP analyses cast doubt on 2 of 9 cases (possibly representing plasmid transmission) but also identified 2 additional cases with isolates highly related (0–3 SNP differences) to other isolates. One case may be the index case: a travel-related case who stayed on the same unit as case 1, 4 months before case 1 detection. The second case stayed in a room previously occupied by 5 cluster A cases. In cluster D, SNP analyses found 1 additional case whose isolate was highly related (ie, 17–19 SNP differences) to other isolates. This case was identified a year before cluster D at another hospital that shares patients with HZ; however, the case’s admission to HZ was after all cluster D cases were detected and no direct epidemiologic link was identified. Conclusions: WGS data can identify cases belonging to transmission clusters that conventional epidemiologic methods missed. Funding: None Disclosures: Allison McGeer reports funds to her institution from Pfizer and Merck for projects for which she is the principal investigator. She also reports consulting fees from Sanofi-Pasteur, Sunovion, GSK, Pfizer, and Cidara
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".