Ceftobiprole and Cefiderocol for Patients on Extracorporeal Membrane Oxygenation: The Role of Therapeutic Drug Monitoring
Bibliographic record
Abstract
INTRODUCTION: Limited data exist on therapeutic ranges for newer antimicrobials in the critically ill, with few pharmacokinetic studies including patients undergoing renal replacement therapy or extracorporeal membrane oxygenation (ECMO). CASE REPRESENTATION: These interventions can potentially alter the pharmacokinetic profile of antibiotics, resulting in therapeutic failures, antimicrobial resistance, or increased toxicity. In this report, we present two ECMO patients treated with cefiderocol and ceftobiprole, where therapeutic drug monitoring (TDM) aided in the successful treatment of severe infections. Antibiotic trough concentrations in both cases were consistent with previously reported therapeutic levels in critically ill and ECMO patients, meeting minimal inhibitory concentrations recommended by the European Committee on Antimicrobial Susceptibility Testing for the respective pathogens. CONCLUSION: Treatment might be suboptimal if doses are not adjusted based on physicochemical properties and extracorporeal support. In an era marked by highly resistant pathogens, these cases highlight the importance of timely access to real-time TDM for optimizing and individualizing antimicrobial treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".