A meta-analysis of controlled studies comparing the association between clozapine and other antipsychotic medications and the development of neutropenia
Bibliographic record
Abstract
BACKGROUND: In most countries, clozapine can only be prescribed with regular monitoring of white blood cell counts because of concerns that clozapine has a stronger association with neutropenia than other antipsychotics. However, this has not been previously demonstrated conclusively with meta-analysis of controlled studies. METHODS: The aim of this study was to assess the strength of the association between clozapine and neutropenia when compared to other antipsychotic medications by a meta-analysis of controlled studies. An electronic search of Medline (1948-2018), PsycINFO (1967-2018) and Embase (1947-2018) using search terms (clozapine OR clopine OR clozaril OR zaponex) AND (neutropenia OR agranulocytosis) was undertaken. Random-effects meta-analysis using Mantel-Haenszel risk ratio was used to assess the strength of the effect size. RESULTS: We located 20 studies that reported rates of neutropenia associated with clozapine and other antipsychotic medications. The risk ratio was not significantly increased in clozapine-exposed groups compared to exposure to other antipsychotic medications (Mantel-Haenszel risk ratio = 1.45, 95% confidence interval = [0.87, 2.42]). This also applied to severe neutropenia (absolute neutrophil count < 500 per µL) when compared to other antipsychotics (Mantel-Haenszel risk ratio = 1.65, 95% confidence interval = [0.58, 4.71]). The relative risk of neutropenia associated with clozapine exposure was not significantly associated with any individual antipsychotic medication. CONCLUSION: Data from controlled trials do not support the belief that clozapine has a stronger association with neutropenia than other antipsychotic medications. This implies that either all antipsychotic drugs should be subjected to haematological monitoring or monitoring isolated to clozapine is not justified.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.075 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".