Clozapine levels as a predictor for therapeutic response: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Clozapine levels may be a more useful predictor of therapeutic response than the dose, given the variability in clozapine metabolism between individuals. We therefore systematically reviewed and meta-analysed the impact of clozapine levels on response and/or relapse to provide guidance on optimal clozapine levels. METHODS: We systematically searched PubMed, PsycInfo and Embase for studies exploring clozapine levels and response and/or relapse. Our primary meta-analysis was rates of response above and below clozapine level thresholds of 350 ng/ml and 600 ng/ml. Secondary analyses were undertaken of mean clozapine levels, dose and concentration/dose (C/D) ratio and response and/or relapse. A meta-regression by study duration was conducted. RESULTS: Twenty studies met inclusion criteria. Clozapine levels above 350 ng/ml were associated with statistically significantly higher rates of response (OR 2.27 95% CI 1.40-3.67, p < 0.001), but not above 600 ng/ml (OR 1.40 95% CI 0.85-2.31, p = 0.19). Higher mean clozapine levels were associated with better rates of response (SMD 0.24, 95% CI 0.00-0.49, p = 0.05), and lower rates of relapse (SMD -0.72, 95% CI -1.26 to -0.19, p = 0.008). By contrast, neither clozapine dose nor C/D ratio was associated with differing rates of response. Similarly, study duration did not affect outcome. CONCLUSIONS: Our findings are in keeping with current guidelines that recommend targeting clozapine levels above 350 ng/ml before augmentation is considered. As some clozapine associated ADRs are dose dependent, levels above 600 ng/ml may have an unfavourable risk-benefit ratio.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".