100. Genomic epidemiology of carbapenemase-producing <i>Enterobacter</i> species in Toronto, Canada, 2007-2021
Bibliographic record
Abstract
Abstract Background Identification of transmission networks of carbapenemase-producing organisms (CPO) is critical to identifying their reservoirs and limiting their spread. This study aimed to use whole-genome sequencing to identify genomic relationships between CP Enterobacter (CP-Ent) in Ontario, Canada over a 15-year period. Methods All CP-Ent cases identified by prospective population-based surveillance in the Toronto/Peel Region from first isolate in 2007 to 2021, from hospital sinks (2016-2019), and wastewater treatment plants and surface water sites (2015-2017) were included. CPO isolates were identified by phenotypic screening (ertapenem MIC &gt; 1mg/L or meropenem disc diffusion diameter ≤ 25mm), confirmed as Enterobacter by MALDI-TOF MS and as positive for carbapenemase genes by PCR, and sequenced by Illumina. Genomics analysis utilized a custom pipeline combining Snippy, IQ-Tree, and ClonalFrameML. Results Overall, 182 isolates from 127 patients, 61 isolates from 40 sinks, and 155 isolates from 9 wastewater plants/surface water sites were available (Table 1). Thirty-one of 127 (24%) patients had &gt; 1 sequenced isolate: in 29 patients all isolates were highly related (median SNV distance 3.5, range 0-13); one patient had two CP-Ent species ∼1.5 years apart, and one had 2 sequence types (STs) 86 days apart. Of 12 (30%) sinks with &gt; 1 isolate, 5 had 2 STs recovered at different times (4 with different carbapenemases). Multiple species, STs, and carbapenemases were recovered from sewage trunks and surface water. Using a ≤ 20 SNV threshold between any one pair of isolates, 14 multi-patient putative clusters involving 39 patients were identified; 1 added cluster was identified and 4 expanded when the threshold was increased to 40 SNVs (Table 2). At the 40 SNV threshold, the median number of patients per cluster was 2 (range 2-9), and the median number of hospitals with at least one first identified patient in each cluster was 1.5 (range 1-4). In total, 35% (45/127) patients were part of a cluster. Conclusion CP-Ent in Ontario are diverse, but a significant minority of affected patients are part of genomically-defined clusters. Genomic clusters may reflect undetected transmission of CP-Ent in healthcare, exposure to water, or other as yet unidentified sources. Disclosures Allison McGeer, MD, AstraZeneca: Honoraria|GSK: Honoraria|Merck: Honoraria|Moderna: Honoraria|Novavax: Honoraria|Pfizer: Grant/Research Support|Pfizer: Honoraria|Roche: Honoraria|Seqirus: Grant/Research Support|Seqirus: Honoraria
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".