Evaluating Ciprofloxacin Dosing for <i>Pseudomonas aeruginosa</i> Infection by Using Clinical Outcome-Based Monte Carlo Simulations
Bibliographic record
Abstract
Pseudomonas aeruginosa causes serious infections whose outcome is highly dependent on antimicrobial therapy. The goal of this study was to predict the relative efficacies of three ciprofloxacin dosing regimens for P. aeruginosa infection using clinical outcome-based Monte Carlo simulations (MCS) with "real patient" demographics, pharmacokinetics, MICs, and pharmacodynamics (PDs). Each cohort consisted of 1,000 simulated study subjects. Three ciprofloxacin dosing regimens were studied, including (i) the recommended standard dose of 400 mg given intravenously (i.v.) every 12 h (q12h), (ii) the recommended high dose of 400 mg i.v. q8h, and (iii) a novel, PD-targeted regimen to attain a fAUC/MIC value of >86. Probability of target attainment (PTA) and probability of cure (POC) were determined for each regimen. POC with the standard dose was at least 0.90 if pathogen MICs were < or =0.25 microg/ml but only 0.59 or 0.27 if MICs were 0.5 or 1 microg/ml, respectively. Predicted cure rates in these MIC categories were significantly higher at 0.72 and 0.40 with the high dose and 0.91 and 0.72 with the PD-targeted regimen(P < 0.0001). Analyses based on the local susceptibility profile produced PTA and POC estimates of 0.44 and 0.74 with the standard ciprofloxacin dose, 0.58 and 0.81 with the high dose, and 0.84 and 0.93 with the PD-targeted regimen, respectively. In conclusion, as demonstrated by clinical outcome-based MCSs, the highest recommended ciprofloxacin dose of 400 mg i.v. q8h should be used in the treatment of P. aeruginosa infection to improve PD target attainment and clinical cure. However, even this appears ineffective if pathogen MICs are 1 mug/ml, warranting the consideration of a lower MIC breakpoint, < or =0.5 microg/ml.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".