185Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use
Bibliographic record
Abstract
Background. Indicators of antimicrobial (AM) use have been described, but the optimal indicator for predicting AM resistance in hospital settings, especially when including pediatric populations, is unknown. This study compared the accuracy of 15 different AM use indicators in the prediction of resistance, in 9 intensive care units (ICUs). Methods. All patients admitted to participating ICUs (3 neonatal, 2 pediatric, 4 adult) between 2006 and 2010 were studied retrospectively. Prevalence and incidence of resistance in endotracheal cultures were both estimated per 4-week period. AM use was measured, per 4-week period, using 15 different indicators. Resistance / AM use combinations studied were resistance to: • methicillin in Staphylococcus aureus / penicillin + 3rd generation cephalosporins + quinolones use • aminoglycosides in coliforms / aminoglycoside use • piperacillin-tazobactam in coliforms / piperacillin-tazobactam use • quinolones in coliforms / quinolones use • carbapenems in E. coli, Klebsiella sp. or Proteus sp. / penicillin + carbapenem + quinolone use • carbapenems in Pseudomonas sp. / carbapenem use • piperacillin-tazobactam in Pseudomonas sp. / piperacillin-tazobactam use • quinolones in Pseudomonas sp. / quinolone use For each combination, indicators of AM use were successively tested in regression models after adjustment for ICU type. Binomial regression was used to model prevalence and Poisson regression, to model incidence. Multiplicative and additive models were tested, as well as no time lag and a 1 period time lag. For each model, the mean absolute error (MAE) was computed. MAEs were then compared using t-tests. Results. A statistical difference between MAEs could only be detected when using carbapenem use for predicting prevalence of resistance to carbapanems in Pseudomonassp. For this combination, the most accurate indicator (courses/100 patient-days, additive model, no time lag; MAE = 0.31 cases/100 admissions) was better (p = 0.0006) than the least accurate indicator (recommended daily doses/100 admissions, multiplicative model, 1-period time lag; MAE = 0.43 cases/100 admissions, thus 38% bigger). Conclusion. In our ICUs, indicators of AM use predicted resistance with similar accuracy, except for one combination. Disclosures. All authors: No reported disclosures.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".