Erythromycin, QTc interval prolongation, and <i>torsade de pointes</i> : Case reports, major risk factors and illness severity
Bibliographic record
Abstract
OBJECTIVES: Erythromycin is a macrolide antibiotic that is widely used for various infections of the upper respiratory tract, skin, and soft tissue. Similar to other macrolides (clarithromycin, azithromycin), erythromycin has been linked to QTc interval prolongation and torsade de pointes (TdP) arrhythmia. We sought to identify factors that link to erythromycin-induced/associated QTc interval prolongation and TdP. METHODS AND RESULTS: In a critical evaluation of case reports, we found 29 cases: 22 women and 7 men (age range 18-95 years). With both oral and intravenous erythromycin administration, there was no significant relationship between dose and QTc interval duration in these cases. Notably, all patients had severe illness. Other risk factors included female sex, older age, presence of heart disease, concomitant administration of either other QTc prolonging drugs or agents that were substrates for or inhibitors of CYP3A4. Most patients had at least two risk factors. CONCLUSIONS: On the basis of case report evaluation, we believe that major risk factors for erythromycin-associated TdP are female sex, heart disease and old age, particularly against a background of severe illness. Coadministration of erythromycin with other drugs that inhibit or are metabolized by CYP3A4 or with QTc prolonging drugs should be avoided in this setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".