Pharmacokinetics of Intravenous Amikacin in Intermittent Hemodialysis: Treatment of Peritoneal Dialysis Peritonitis Caused by Nontuberculous Mycobacterium (NTM)
Bibliographic record
Abstract
Background. Data on aminoglycoside (AG) pharmacokinetics (PK) in intermittent hemodialysis (iHD) is outdated; current practice employs more efficient iHD modalities. Once-daily AG are used to optimize treatment of resistant organisms including non-tuberculous mycobacterium (NTM). Currently recommended amikacin dosing (3-5 mg/kg post-iHD) is unlikely to obtain IDSA-recommended target Cmax for NTM (20–30mg/L). Recently, experts have suggested gentamicin dosed pre-iHD can attain maximal peak to optimize PK/PD goals; using iHD to decrease overall exposure to avoid toxicity. No PK data has described pre-iHD dosing strategy for high-dose amikacin, so it remains uncommonly practiced. Methods. We describe a high-dose strategy of IV amikacin and PK monitoring (efficacy and toxicity) in 2 cases of peritoneal dialysis peritonitis caused by NTM. Amikacin was dosed at 7 mg/kg on iHD days, given 4 hours pre-iHD to allow distribution. Amikacin levels were drawn: prior to iHD (peak); 4 hours post-iHD (trough); and prior to next dose administration (“true trough” to assess non-iHD clearance). PK analysis was performed assuming one compartment, linear kinetics. Results. See figure 1 and figure 2. Conclusion. Pre-iHD amikacin 7 mg/kg achieved IDSA-recommended Cmax for treatment of NTM peritonitis. Amikacin clearance with current iHD modalities was much more efficient than prior reports. High-dose, pre-iHD administration strategy successfully improved PK/PD parameters, allowing high Cmax, while decreasing overall amikacin exposure on non-iHD days. We suggest pre-iHD amikacin dosing for iHD patients who require high-dose therapy for optimal attainment of Cmax targets in serious infections. Disclosures. All authors: No reported disclosures
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".