Clarithromycin, QTc interval prolongation and <i>torsades de pointes</i> : the need to study case reports
Bibliographic record
Abstract
BACKGROUND: The manufacturers of clarithromycin sought a drug similar in efficacy to erythromycin but with a superior side-effect profile. They generally achieved this outcome, but postmarketing findings identified a series of reports linking clarithromycin to QTc interval prolongation and torsades de pointes (TdP) ultimately leading to a Black Box Warning. We sought to clarify risk factors associated with TdP among case reports of patients receiving clarithromycin linked to QTc interval prolongation and TdP. METHODS AND RESULTS: In a detailed literature search, we found 15 women, five men, and one boy meeting our search criteria. Among the 17 adults with reported clarithromycin dose and concurrent QTc interval measurement, we found no statistically significant relationship between clarithromycin dose and QTc interval duration. This did not change for the adults who developed TdP. Among adults, major risk factors were female sex (15), old age (11) and heart disease (17). A total of eight adult subjects had all three major risk factors and 14 of the 20 adults had at least two major risk factors. All adult subjects had at least two risk factors besides clarithromycin. A total of four of the 20 adults received cisapride and three received disopyramide. Three adults were considered to suffer from some aspect of the congenital long QT syndrome. CONCLUSIONS: We believe that the risk factor description for this drug should be refined to emphasize the major risk factors of (1) female sex, (2) old age and (3) heart disease.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".