External Evaluation of Longitudinal Population Pharmacokinetic Models of Vancomycin in Patients With Osteoarticular Infections
Bibliographic record
Abstract
BACKGROUND: Osteoarticular infections pose a challenge for therapeutic drug monitoring of vancomycin because they often require prolonged treatment. Given the extensive renal elimination of vancomycin, its pharmacokinetic properties are difficult to predict in the later stages of treatment because the risk of nephrotoxicity increases with the duration of treatment. In this study, published longitudinal population pharmacokinetic (popPK) models were externally evaluated in a cohort of patients with osteoarticular infections. METHODS: A literature search was performed in PubMed/EMBASE and published reviews. The predictive performance of the selected models was assessed through prediction- and simulation-based diagnostics using NONMEM software. Data were collected during both the retrospective and prospective phases, during which prospectively recruited patients provided additional vancomycin concentrations. RESULTS: The external validation dataset comprised 525 vancomycin concentrations obtained from 73 patients treated for osteoarticular infections at Montréal General Hospital. Two published popPK models that provided different approaches for integrating a longitudinal structure were identified. Both failed to meet the clinically acceptable threshold of imprecision in population predictions. The weighted median absolute prediction error ranged from 34.9% to 48.3% before re-estimation of model parameters and from 33.5% to 35.2% after re-estimation. The re-estimated models tended to underpredict vancomycin concentrations in the later stages of treatment. CONCLUSIONS: The 2 evaluated models showed poor predictive performance in our local study population. Further studies should explore new strategies to incorporate a longitudinal component and consider other relevant clinical covariates to develop improved longitudinal popPK models for vancomycin.
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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) | 0.000 | 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".