Carbapenem-resistantAcinetobacter baumanniiat a hospital in Botswana: Detecting a protracted outbreak using whole genome sequencing
Bibliographic record
Abstract
Abstract Carbapenem-resistant Acinetobacter baumannii (CRAb) has emerged as a major and often fatal cause of bloodstream infections among hospitalized patients in low- and middle-income countries (LMICs). CRAb outbreaks are hypothesized to arise from reservoirs in the hospital environment, but outbreak investigations in LMICs are seldom able to incorporate whole genome sequencing (WGS) due to resource limitations. We performed WGS at the National Institute for Communicable Diseases (Johannesburg, South Africa) on stored A. baumannii isolates (n=43) collected during 2021–2022 from a 530-bed referral hospital in Gaborone, Botswana where CRAb infection incidence was noted to be rising. This included blood culture isolates from patients (aged 2 days – 69 years), and environmental isolates collected at the hospital's 33-bed neonatal unit. Multilocus sequence typing (MLST), antimicrobial/biocide resistance gene identification, and phylogenetic analyses were performed using publicly accessible analysis pipelines. Single nucleotide polymorphism (SNP) matrices were used to assess clonal lineage. MLST revealed 79% of isolates were sequence type 1 (ST1), including all 19 healthcare-associated blood isolates and three out of five environmental isolates. Genes encoding for carbapenemases ( bla NDM-1 , bla OXA-23 ) and biocide resistance ( qacE ) were present in all 22 ST1 isolates. Phylogenetic analysis of the ST1 clade demonstrated spatial clustering by hospital unit. Nearly identical isolates spanned wide ranges in time (>1 year), suggesting ongoing transmission from environmental sources. One highly similar clade (average difference of 2.3 SNPs) contained all eight neonatal blood isolates and three environmental isolates from the neonatal unit. These results were critical in identifying environmental reservoirs (e.g. sinks) and developing remediation strategies. Using a phylogenetically informed approach, we also identified diagnostic genes useful for future tracking of outbreak clones without the need for WGS. This work highlights the power of South-South and South-North partnerships in building public health laboratory capacity in LMICs to detect and contain the spread of antimicrobial resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".