Population-based study of long-term mortality risk associated with clozapine use among patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia have a significantly elevated risk of mortality. Clozapine is effective for treatment-resistant schizophrenia, but its use is limited by side-effects. Understanding its association with mortality risk is crucial. AIMS: To investigate the associations of clozapine with all-cause and cause-specific mortality risk in schizophrenia patients. METHOD: In this 18-year population-based cohort study, we retrieved electronic health records of schizophrenia patients from all public hospitals in Hong Kong. Clozapine users (ClozUs) comprised schizophrenia patients who initiated clozapine treatment between 2003 and 2012, with the index date set at clozapine initiation. Comparators were non-clozapine antipsychotic users (Non-ClozUs) with the same diagnosis who had never received a clozapine prescription. They were 1:2 propensity score matched with demographic characteristics and physical and psychiatric comorbidities. ClozUs were further defined according to continuation of clozapine use and co-prescription of other antipsychotics (polypharmacy). Accelerated failure time (AFT) models were used to estimate the risk of all-cause and cause-specific mortality (i.e. suicide, cardiovascular disease, infection and cancer). RESULTS: This study included 9,456 individuals (mean (s.d.) age at the index date: 39.13 (12.92) years; 50.73% females; median (interquartile range) follow-up time: 12.37 (9.78-15.22) years), with 2020 continuous ClozUs, 1132 discontinuous ClozUs, 4326 continuous non-ClozUs and 1978 discontinuous Non-ClozUs. Results from adjusted AFT models showed that continuous ClozUs had a lower risk of suicide mortality (acceleration factor 3.01; 99% CI: 1.41-6.44) compared with continuous Non-ClozUs. Continuous ClozUs with co-prescription of other antipsychotics exhibited lower risks of suicide mortality (acceleration factor 3.67; 1.41-9.60) and all-cause mortality (acceleration factor 1.42; 1.07-1.88) compared with continuous Non-ClozUs. No associations were found between clozapine and other cause-specific mortalities. CONCLUSIONS: These results add to the existing evidence on the effectiveness of clozapine, particularly its anti-suicide effects, and emphasise the need for continuous clozapine use for suitable patients and the possible benefit of clozapine polypharmacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".