Selective serotonin reuptake inhibitors and <i>torsade de pointes</i> : new concepts and new directions derived from a systematic review of case reports
Bibliographic record
Abstract
OBJECTIVE: In the light of the recent United States Food and Drug Administration (FDA) warning to clinicians on using previously approved doses of citalopram because of the purported higher risk of torsade de pointes (TdP), we pursued the broader question: are selective serotonin reuptake inhibitor (SSRI) antidepressant agents as a group unsafe because they might induce QTc interval prolongation and TdP? METHOD: We reviewed the literature and found only 15 case reports (6 of fluoxetine, 1 of sertraline and 8 of citalopram) of SSRI-associated QTc interval prolongation linking to TdP. RESULTS: A total of 13 cases contained sufficient information for analysis. In the setting of TdP, QTc interval prolongation does not clearly relate to SSRI dose. CONCLUSION: Applying conventional statistics as the FDA does may not be the best tool to study this phenomenon because SSRI-associated TdP is a very rare event and hence best understood as an 'extreme outlier'. Despite the limitations inherent in case report material, case reports on drug-associated QTc interval prolongation and TdP provide valuable information that should be considered along with other sources of information for clinical guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".