Monitoring of antipsychotic plasma levels in the assessment of poor response and nonadherence to antipsychotics in delusional disorder
Bibliographic record
Abstract
Introduction Over the last decades, antipsychotic plasma levels have been used to evaluate therapeutic response, adherence and safety of antipsychotics in schizophrenia. Their clinical utility in delusional disorder (DD) has been poorly studied. Objectives To investigate the relationship between plasma concentrations of risperidone (R), 9-OH-risperidone (9-OH-R) and olanzapine (OLZ), and clinical outcomes in DD. Methods Case-series of inpatients and outpatients with DD receiving treatment with risperidone (n=19) or olanzapine (n=2). Determination of R, 9-OH-R (active metabolite) and OLZ levels were obtained by high-performance liquid chromatography with electrochemical detection. Clinical variables such as treatment response or adverse events were recorded for all patients. These variables were correlated with two plasmatic ratios in patients treated with R: R:9-OH-R concentration ratio and total concentration-to-dose (C: D) ratio, indicating CYP2D6 activity and R elimination respectively. Results Twenty-one patients were included: inpatients (n=10) and outpatients (n=11). Dose range: R, 1-6 mg/day; OLZ, 5-10 mg/day. Three outpatients (R, n=2; OLZ, n=1) presented antipsychotic levels under the detection limit (non-adherence). All R patients showed CYP2D6 activity (R: 9-OH-R ratio <1). Eight patients presented C: D > 14, indicating a reduction of R elimination, which was associated with poor clinical response (n=3), adverse events (n=3) and no clinical relevance (n=2). OLZ (n=2), no association between levels and clinical outcomes. Conclusions The determination of antipsychotic plasma levels may be of clinical utility in the assessment of treatment resistance, antipsychotic-adverse events or non-adherence in inpatients or outpatients with DD. Therapeutic drug monitoring should be further studied in future works. Disclosure AGR has received honoraria, registration for congresses and/or travel costs from Janssen, Lundbeck-Otsuka and Angelini.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".