Using the Association Between Antibiotic Susceptibility and Genetic Relatedness to Rescue Old Drugs for Empiric Use
Bibliographic record
Abstract
Abstract Background Rising rates of antibiotic resistance have led to the use of broader spectrum antibiotics and increasingly compromise empiric therapy. Knowing the antibiotic susceptibility of a pathogen’s close genetic relative(s) may improve empiric antibiotic selection. Methods Using genomic and phenotypic data from three separate clinically-derived databases of Escherichia coli isolates, we evaluated multiple genomic methods and statistical models for predicting antibiotic susceptibility, focusing on potentially rapidly available information such as lineage or genetic distance from archived isolates. We applied these methods to derive and validate prediction of antibiotic susceptibility to common antibiotics. Results We evaluated 968 separate episodes of suspected and confirmed infection with Escherichia coli from three geographically and temporally separated databases in Ontario, Canada, from 2010-2018. The most common sequence type (ST) was ST131 (30%). Antibiotic susceptibility to ciprofloxacin and trimethoprim-sulfamethoxazole were lowest (<=72%). Across all approaches, model performance (AUC) ranges for predicting antibiotic susceptibility were greatest for ciprofloxacin (0.76-0.97), and lowest for trimethoprim-sulfamethoxazole (0.51-0.80). When a model predicted a susceptible isolate, the resulting (post-test) probabilities of susceptibility were sufficient to warrant empiric therapy for most antibiotics (mean 92%). An approach combining multiple models could permit the use of narrower spectrum oral agents in 2 out of every 3 patients while maintaining high treatment adequacy (∼90%). Conclusions Methods based on genetic relatedness to archived samples in E. coli could be used to rescue older and typically unsuitable agents for use as empiric antibiotic therapy, as well as improve decisions to select newer broader spectrum agents. Summary Rapid genomic approaches that capitalize on the association between genetic relatedness and phenotype can improve our selection of antibiotics, allowing us to rescue older drugs for empiric use and better select newer and broader spectrum agents.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 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".