Support for higher ciprofloxacin AUC24/MIC targets in treating Enterobacteriaceae bloodstream infection
Bibliographic record
Abstract
OBJECTIVES: Given concerns regarding optimal therapy for serious Gram-negative infections, the goal was to characterize the pharmacodynamics of ciprofloxacin in the context of treating bloodstream infection. PATIENTS AND METHODS: Data were collected from the medical records of 178 clinical cases. Blood isolates were retrieved and ciprofloxacin MICs were measured. Forty-two cases in which ciprofloxacin was initiated within 24 h of the positive blood culture were used in the pharmacodynamic analysis. RESULTS: Significant factors with regard to treatment failure were low ciprofloxacin AUC(24)/MIC (P < 0.0001), high MIC (P = 0.001), male sex (P = 0.002) and low AUC(24) (P = 0.01). AUC(24)/MIC (P = 0.012) and MIC (P = 0.019) were significant variables in multivariate analyses; however, only the former remained significant (P = 0.038) after excluding two cases with ciprofloxacin-resistant isolates. An AUC(24)/MIC breakpoint of 250 was most significant, with cure rates of 91.4% (32/35) and 28.6% (2/7) in patients with values above and below this threshold, respectively (P = 0.001). The risk of ciprofloxacin treatment failure was 27.8 times (95% confidence interval, 2.1-333) greater in those not achieving an AUC(24)/MIC >or=250 (P = 0.011). Monte Carlo simulation of 5000 study subjects predicted that 0.88 of the population would achieve an AUC(24)/MIC >or=250 with standard-dose ciprofloxacin (400 mg intravenously every 12 h). CONCLUSIONS: This study confirms the pharmacodynamic parameters of ciprofloxacin that are important for optimizing the treatment of serious infections, particularly the benefits of achieving an AUC(24)/MIC >or=250, rather than the conventional target of >or=125. It also shows the relevance of dose selection in optimizing target attainment, with important differences among pathogens, even those with MICs within the susceptible range.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".