Previous Antibiotic Exposure and Antimicrobial Resistance in Invasive Pneumococcal Disease: Results From Prospective Surveillance
Bibliographic record
Abstract
BACKGROUND: Estimating the risk of antibiotic resistance is important in selecting empiric antibiotics. We asked how the timing, number of courses, and duration of antibiotic therapy in the previous 3 months affected antibiotic resistance in isolates causing invasive pneumococcal disease (IPD). METHODS: We conducted prospective surveillance for IPD in Toronto, Canada, from 2002 to 2011. Antimicrobial susceptibility was measured by broth microdilution. Clinical information, including prior antibiotic use, was collected by chart review and interview with patients and prescribers. RESULTS: Clinical information and antimicrobial susceptibility were available for 4062 (90%) episodes; 1193 (29%) of episodes were associated with receipt of 1782 antibiotic courses in the prior 3 months. Selection for antibiotic resistance was class specific. Time elapsed since most recent antibiotic was inversely associated with resistance (cephalosporins: adjusted odds ratio [OR] per day, 0.98; 95% confidence interval [CI], .96-1.00; P = .02; macrolides: OR, 0.98; 95% CI, .96-.99; P = .005; penicillins: OR [log(days)], 0.62; 95% CI, .44-.89; P = .009; fluoroquinolones: profile penalized-likelihood OR [log(days)], 0.62; 95% CI, .39-1.04; P = .07). Risk of resistance after exposure declined most rapidly for fluoroquinolones and penicillins and reached baseline in 2-3 months. The decline in resistance was slowest for macrolides, and in particular for azithromycin. There was no significant association between duration of therapy and resistance for any antibiotic class. Too few patients received multiple courses of the same antibiotic class to assess the significance of repeat courses. CONCLUSIONS: Time elapsed since last exposure to a class of antibiotics is the most important factor predicting antimicrobial resistance in pneumococci. The duration of effect is longer for macrolides than other classes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".