Rapid inference of antibiotic susceptibility phenotype of uropathogens using metagenomic sequencing with neighbor typing
Bibliographic record
Abstract
ABSTRACT Timely diagnostic tools are needed to improve antibiotic treatment. Pairing metagenomic sequencing with genomic neighbor typing algorithms may support rapid clinically actionable results. We created resistance-associated sequence elements (RASE) databases for Escherichia coli and Klebsiella spp . and used them to predict antibiotic susceptibility in directly sequenced (Oxford Nanopore) urine specimens from critically ill patients. RASE analysis was performed on pathogen-specific reads from metagenomic sequencing. We evaluated the ability to predict (i) multi-locus sequence type (MLST) and (ii) susceptibility profiles. We used neighbor typing to predict MLST and susceptibility phenotype of E. coli (64/80) and Klebsiella spp . (16/80) from urine samples. When optimized by lineage score, MLST predictions were concordant for 73% of samples. Similarly, a RASE-susceptible prediction for a given isolate was associated with a specificity and a positive likelihood ratio (LR+) for susceptibility of 0.65 (95% CI, 0.54–0.76) and 2.26 (95% CI, 1.75–2.92), respectively, with an increase in the probability of susceptibility of 10%. A RASE-non-susceptible prediction was associated with a sensitivity and a negative likelihood ratio (LR-) for susceptibility of 0.79 (95% CI, 0.74–0.84) and 0.32 (95% CI, 0.24–0.43) respectively, with a decrease in the probability of susceptibility of 20%. Numerous antibiotic classes could reasonably be reconsidered empiric therapy by shifting empiric probabilities of susceptibility across relevant treatment thresholds. Moreover, these predictions can be available within 6 h. Metagenomic sequencing of urine specimens with neighbor typing provides rapid and informative predictions of lineage and antibiotic susceptibility with the potential to impact clinical decision-making. IMPORTANCE Urinary tract infections (UTIs) are a common diagnosis in hospitals and are often treated empirically with broad-spectrum antibiotics. These broad-spectrum agents can select for resistance in these bacteria and co-colonizing organisms. The use of narrow-spectrum agents is desirable as an antibiotic stewardship measure; however, it is counterbalanced by the need for adequate therapy. Identification of causative organisms and their antibiotic susceptibility can help direct treatment; however, conventional testing requires days to produce actionable results. Methods to quickly and accurately predict susceptibility phenotypes for pathogens causing UTI could thus improve both patient outcomes and antibiotic stewardship. Here, expanding on previous work showing accurate prediction for certain Gram-positive pathogens, we demonstrate how the use of RASE from metagenomic sequencing can provide informative and rapid phenotype prediction results for common Gram-negative pathogens in UTI, highlighting the future potential of this method to be used in clinical settings to guide empiric antibiotic selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".