Introducing the Escalation Antibiogram: A Simple Tool to Inform Changes in Empiric Antimicrobials in the Nonresponding Patient
Bibliographic record
Abstract
BACKGROUND: Hospital antibiograms guide initial empiric antibiotic treatment selections, but do not directly inform escalation of treatment among nonresponding patients. METHODS: Using gram-negative bacteremia as an exemplar condition, we sought to introduce the concept of an escalation antibiogram. Among episodes of gram-negative bacteremia between 2017 and 2020 from 6 hospitals in the Greater Toronto Area, we generated escalation antibiograms for each of 12 commonly used agents. Among organisms resistant to that antibiotic, we calculated the likelihood of susceptibility to each of the other 11 agents. In subgroup analyses, we examined escalation antibiograms across study years, individual hospitals, community versus hospital onset, and pathogen type. RESULTS: Among 6577 gram-negative bacteremia episodes, the likelihood of coverage was ampicillin 31.8%, cefazolin 62.7%, ceftriaxone 67.1%, piperacillin-tazobactam 72.5%, ceftazidime 74.1%, trimethoprim-sulfamethoxazole 74.4%, ciprofloxacin 77.1%, tobramycin 88.3%, gentamicin 88.8%, ertapenem 91.0%, amikacin 97.5%, and meropenem 98.2%. The escalation antibiograms revealed marked shifts in likelihood of coverage by the remaining 11 agents. For example, among ceftriaxone-resistant isolates, piperacillin-tazobactam susceptibility (21.2%) was significantly lower than trimethoprim-sulfamethoxazole (54.2%, P < .0001), ciprofloxacin (63.0%, P < .0001), ertapenem (73.4%, P < .0001), tobramycin (80.1%, P < .0001), gentamicin (82.8%, P < .0001), meropenem (94.3%, P < .0001), and amikacin (97.1%, P < .0001). Trimethoprim-sulfamethoxazole was the second-ranked agent in the meropenem escalation antibiogram (49.6%) and first in the amikacin escalation antibiogram (86.0%). Escalation antibiograms were consistent across 4 study years and 6 hospitals. CONCLUSIONS: Escalation antibiograms can be generated to inform empiric treatment changes in nonresponding patients. These tools can yield important insights such as avoiding the common maneuver of escalating from ceftriaxone to piperacillin-tazobactam in suspected gram-negative bacteremia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".