Amikacin nomogram for treatment of adult cystic fibrosis exacerbations based on an external evaluation of a population pharmacokinetic model
Bibliographic record
Abstract
BACKGROUND: In patients with cystic fibrosis (CF), amikacin is the alternative for the treatment of acute pulmonary exacerbations associated with pathogens resistant to tobramycin. Population pharmacokinetic (PK) models of amikacin in adult patients with CF have been previously published. However, current dosing recommendations remain disputed (Illamola et al. Clin Pharmacokinet. 2018;57(10):1217-1228). We perform here the first external evaluation of a published amikacin adult CF population PK model and propose a dosing nomogram for initial dosing. METHODS: We retrospectively collected demographic, biological, and clinical data from the medical records of adult patients who had received intravenous amikacin. To assess the predictive performance of this model we applied visual comparison of predictions to observations, calculation of bias and inaccuracy, and simulation-based diagnostics. Monte Carlo simulations from the evaluated model were used to compare maximum concentration/minimum inhibitory concentration achieved with different dosing regimens. RESULTS: A total of 91 concentrations from 19 adult patients with CF were collected for external evaluation. The model predicted amikacin concentrations with reasonable bias (7.2% [95% confidence interval, CI: -0.7% to 15.0%]) and inaccuracy (18.2% [95% CI: 12.0%-24.4%]). Our simulations with this model suggest that administered amikacin doses must be adjusted to creatinine clearance and also adjusted to body weight (doses from 20 to 45 mg/kg/d). According to these simulations, we developed the Montreal amikacin nomogram to optimize amikacin dosing regimens in patients with CF. CONCLUSION: In conclusion, we developed the first nomogram to optimize initial amikacin dosing regimens in patients with CF based on this external evaluation of a recently published amikacin population PK model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".