Reevaluating Clozapine-Induced QT Prolongation
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Clozapine is the most effective medicine for treatment-resistant schizophrenia, but is limited by adverse events, including potential QT prolongation which can lead to life-threatening arrhythmias. Studies linking clozapine and corrected QT (QTc) prolongation may overestimate this risk due to high rates of clozapine-associated tachycardia. We investigated whether trough clozapine plasma levels are independently associated with QT prolongation after accounting for heart rate. STUDY DESIGN: We conducted a retrospective, cross-sectional analysis of inpatients treated with clozapine at a tertiary hospital between 2017 and 2023. Trough clozapine plasma levels, and 12-lead electrocardiograms were extracted from electronic medical records. QT intervals were manually measured and corrected using Bazett's, Fredericia, Hodges' formulae, and the QT nomogram. Multivariable regression and causal mediation were used to test the association between clozapine plasma level, heart rate, and QTc. STUDY RESULTS: Among 313 patients, Bazett's correction classified 27.5% as having prolonged QTc, whereas only one patient (0.3%) exceeded the at-risk threshold using Fredericia, Hodges, or the QT nomogram. Clozapine plasma level correlated with Bazett's-corrected QT (QTcB) (P = .02), but not after adjustment for heart rate (P = .75). Mediation analysis showed that heart rate significantly mediated the relationship between clozapine plasma level and QTcB intervals (P < .001). CONCLUSIONS: Apparent clozapine-induced QTc prolongation is largely an artifact of tachycardia and over-correction by Bazett's formula. The Fredericia and Hodges formulae, and the QT nomogram provide a more reliable assessment of torsadogenic risk and prevent unnecessary discontinuation or dose reductions of clozapine.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".