Comparative simulation of intraperitoneal aminoglycoside regimens for patients with peritonitis on automated peritoneal dialysis
Bibliographic record
Abstract
BACKGROUND: Intraperitoneal (IP) aminoglycosides (AGs) continue to be the cornerstone of empiric management of peritonitis. AG dosing during automated peritoneal dialysis (APD), however, has not been well studied in patients with peritonitis. We sought to identify differences in AG exposure in the peritoneum and plasma for two different dosing regimens with little supporting evidence in patients on APD with peritonitis. METHODS: A retrospective design that utilised the peritoneal and plasma concentration-time data from a prior study of 18 continuous ambulatory peritoneal dialysis (CAPD) patients with peritonitis to generate an in silico peritoneal and plasma PK model. This model was then used to compare via simulation using Phoenix© WinNonlin Software with IP AG dosing for a loading-dose regimen (1.5 mg/kg first dose) versus a fixed-dose regimen (0.6 mg/kg/d) in patients on APD with peritonitis. RESULTS: Outcome measures were (1) percentage of time where peritoneal peak concentrations/minimal inhibitory concentration (MIC) ratio >10, (2) AUC/MIC > 74 and (3) plasma Cmin concentrations. Both regimens resulted in > 90% optimal peak/MIC ratio and AUC/MIC ratios on days 1 and 5 of the dose protocol. The loading-dose regimen resulted in IP exposures that were 2.5 times greater in the peritoneal compartment on day 1. By day 5, both protocols resulted in similar accumulation of AG plasma Cmin concentrations of 2.5-3.4 mg/L versus 2.4-3.3 mg/L, respectively, for the loading-dose regimen versus fixed-dose regimen. CONCLUSIONS: The current international guidelines for the treatment of peritoneal dialysis-associated peritonitis can continue to recommend the fixed-dose regimen for those on APD with the addition of plasma Cmin monitoring after 3 days to assess for drug accumulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".