Antipsychotic Polypharmacy and Corrected QT Interval: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: It remains unclear whether antipsychotic polypharmacy, a common clinical practice, is related to an increased risk of corrected time between start of Q wave and end of T wave (QTc) interval prolongation. We conducted a systematic review of the literature to address this important issue. METHOD: A systematic literature search was conducted in October 2014, using MEDLINE, Embase, and PsycINFO. Studies and case reports were included if they reported QTc intervals or QTc interval changes before and after antipsychotic polypharmacy or QTc intervals in both antipsychotic polypharmacy and monotherapy groups. RESULTS: A total of 21 articles (10 clinical trials, 4 observational studies, and 7 case reports) met inclusion criteria. The clinical trials have shown that a combination treatment with risperidone or pimozide is not obviously related to an increase in QTc interval, whereas ziprasidone or sertindole combined with clozapine may prolong QTc interval. Among the 4 observational studies, antipsychotic polypharmacy was not clearly associated with QTc prolongation in 3 studies, each cross-sectional. In contrast, one prospective study showed a significant increase in QTc interval following antipsychotic coadministration. The case reports indicated an increased risk of QTc prolongation in at least some patients receiving antipsychotic polypharmacy. CONCLUSIONS: Currently available evidence fails to confirm that antipsychotic polypharmacy worsens QTc prolongation in general, although the evidence is scarce and inconsistent. Clinicians are advised to remain conservative in resorting to antipsychotic polypharmacy, as a combination of some QTc-prolongation liable antipsychotics may further prolong QTc interval, and efficacy supporting the clinical benefits of antipsychotic polypharmacy is equivocal, at best.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".