Clinical Importance of Monitoring Unbound Valproic Acid Concentration in Patients with Hypoalbuminemia
Bibliographic record
Abstract
STUDY OBJECTIVE: Because the pharmacokinetic evaluation of valproic acid (VPA) based on total drug concentration may be misleading in patients with hypoalbuminemia as a result of saturable protein binding and saturable metabolism, we sought to investigate the usefulness of therapeutic drug monitoring of unbound VPA concentration in a real-world clinical context, with a focus on clinically significant neurologic adverse outcomes. DESIGN: Retrospective analysis. SETTING: Large academic tertiary care hospital in Montreal, Canada. PATIENTS: Forty-one adults, hospitalized or followed as outpatients, for whom unbound VPA concentration testing was performed between January 1, 2008, and April 30, 2015. Patients were retrospectively identified by using the hospital's central laboratory database. MEASUREMENTS AND MAIN RESULTS: In the multiple linear regression analysis, the two variables that significantly predicted unbound VPA concentration were total VPA concentration (p<0.001) and albumin concentration (p<0.001). The correlation between total VPA concentration and the number of neurologic adverse symptoms was 0.187 (p=0.241), whereas the correlation between unbound VPA concentration and the number of neurologic adverse symptoms was 0.384 (p=0.013). The performance of total and unbound VPA concentrations in predicting the presence of at least one neurologic adverse symptom, as determined by the receiver operating characteristic curve, was 0.642 (95% confidence interval [CI] 0.449-0.836, p=0.167) and 0.776 (95% CI 0.629-0.923, p=0.007), respectively. CONCLUSION: This study showed that in the presence of hypoalbuminemia, high unbound VPA concentrations can be observed despite normal or low total VPA concentrations. It also demonstrated that high unbound VPA concentrations are associated with clinically significant neurologic adverse symptoms. Clinicians should be aware that unbound VPA concentration monitoring may be required in the presence of hypoalbuminemia.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".