Improving Decision Making in Empiric Antibiotic Selection (IDEAS) for Gram-negative Bacteremia: A Prospective Clinical Implementation Study
Bibliographic record
Abstract
BACKGROUND: Timely selection of adequate empiric antibiotics has become increasingly difficult due to rising resistance rates and the competing desire to apply antimicrobial stewardship (AMS) principles. Individualized clinical prediction models offer the promise of reducing broad-spectrum antibiotic use and preserving/improving adequacy of treatment, but few have been validated in the clinical setting. METHODS: Multivariable models were used to predict the probability of susceptibility for gram-negative (GN) bacteria in bloodstream infections (bacteremia) to ceftriaxone, ciprofloxacin, ceftazidime, piperacillin-tazobactam, and meropenem. The models were combined with existing resistance-prediction methods to generate optimized and individualized suggestions for empiric therapy that were provided to prescribers by an AMS pharmacist. De-escalation of empiric antibiotics and adequacy of therapy were analyzed using a quasi-experimental design comparing two 9-month periods (pre- and postintervention) at a large academic tertiary care institution. RESULTS: Episodes of bacteremia (n = 182) were identified in the preintervention and postintervention (n = 201) periods. Patients who received the intervention were more likely to have their therapy de-escalated (29 vs 21%; aOR = 1.77; 95% CI, 1.09-2.87; P = .02). The intervention also increased the proportion of patients who were on the narrowest adequate therapy at the time of culture finalization (44% in the control and 55% in the intervention group; aOR = 2.04; 95% CI, 1.27-3.27; P = .003). Time to adequate therapy was similar in the intervention and control groups (5 vs 4 hours; P = .95). CONCLUSIONS: An AMS intervention, based on individualized predictive models for resistance, can influence empiric antibiotic selections for GN bacteremia to facilitate early de-escalation of therapy without compromising adequacy of antibiotic coverage.
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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 | 0.040 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".