Interpreting whole-genome sequencing data during neonatal Klebsiella oxytoca complex outbreak management
Bibliographic record
Abstract
BACKGROUND: K. oxytoca generally has a benign susceptibility profile and low virulence but can cause invasive infections in vulnerable populations, like preterm infants. We aim to describe how whole-genome sequencing (WGS) was used to inform management of a prolonged K. oxytoca outbreak on a neonatal intensive care unit (NICU) and implications for outbreak response involving similar organisms. METHODS: We retrospectively reviewed outbreak-associated clinical and environmental isolates from a Swiss NICU. WGS was used to track evolution of resistance and highlighted multiple concurrent outbreaks. WGS was performed using a MiSeq or NextSeq 500 Illumina sequencer. The resulting genome sequences were analysed using Ridom SeqSphere. The current report conforms to ORION reporting guidelines. RESULTS: Of 152 Klebsiella spp. patient-derived isolates, 83 were genotyped using WGS, along with six environmental isolates. This confirmed two outbreak waves (November 2021-February 2022, ST18 wildtype; July 2022-June 2023, main cluster ST18 KI β-lactamase hyperproducer), with multiple genotypically connected clusters during the second wave. Confirmed sepsis (K. oxytoca ST18 wildtype) occurred in four preterm or low birthweight infants. Twins presented a genotypically identical ST with a different susceptibility phenotype (ST18 wildtype vs. K1 OXY-hyperproducer). WGS combined with epidemiological investigation and environmental sampling identified an environmental source. There was a second outbreak wave after source removal, presumably due to the prolonged presence of colonised infants with typically long NICU stays and insufficient standard infection prevention and control measures to prevent transmission. CONCLUSION: WGS use in NICU outbreaks involving low-virulence bacteria can support identification and removal of potentiating environmental sources. These measures, however, will often be insufficient to contain the outbreak, and ongoing WGS surveillance of ubiquitous species may uncover multiple concurrent outbreaks, presumably driven by continuing transfer-transmission between different sources and infants in the NICU. Maximising standard infection prevention and control (IPC) measures is appropriate in this context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".