Neighbour Typing Using Long Read Sequencing Provides Rapid Prediction of Sequence Type and Antimicrobial Susceptibility of <i>Klebsiella pneumoniae</i>
Bibliographic record
Abstract
ABSTRACT The rapid genome-based diagnostic approach of long read sequencing coupled with neighbor typing offers the potential to improve empiric treatment of infection. However, this approach is still in development, and clinical validation is needed to support its use. In this study, we present an assessment of a neighbour typing method (RASE - resistance associated sequence elements) to predict lineage or sequence type (ST) and antimicrobial susceptibility in real time for Klebsiella pneumoniae sensu lato. We analysed the initial reads generated during the early phase of long read sequencing from pure culture (n=99), mock communities (n=20) and metagenomic samples (n=20). RASE accurately identified 69.7% and 70% of STs in pure culture and metagenomes, respectively, and identified the STs of the isolates representing the highest proportion in mock communities. Regarding antimicrobial susceptibility prediction, the probability of susceptibility increased to 72% (95% CI 63%-80%) across all tested antibiotics, when RASE predicted susceptibility, and decreased probability of susceptibility to 8.9% (95% CI 6.4%-9.6%) when was indicative of a resistant phenotype. Our study confirmed that genomic neighbor typing in K. pneumoniae sensu lato is capable of providing informative predictions of ST and antibiotic susceptibility in less than ten minutes (after the start of sequencing) with 200-500 reads. IMPORTANCE The growing burden of antimicrobial resistance is leading to high rates of mortality and morbidity worldwide. This situation has made the selection of empirical antibiotic therapy challenging, due to the risk of treatment failure and the overuse of last-resort antibiotics. The development of new sequencing technologies is helping to reduce the waiting time for a microbiological diagnosis, providing information in the early phase of bacterial infections, which could help improve clinical outcomes in a time of rising antimicrobial resistance. In this context, we assessed the performance of RASE (resistance associated sequence elements) in Klebsiella pneumoniae , an opportunistic pathogen frequently associated with nosocomial infections, which can rapidly acquire antibiotic resistance genes. Thus, in our study we provide insights that may aid in the validation of RASE for clinical use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".