QT Prolongation Associated with Multiple Drug-Drug Interactions
Bibliographic record
Abstract
Many drugs are known to prolong QT interval which increases the risk of development of ventricular tachycardia and cardiac arrest. The majority of sudden cardiac deaths are caused by acute ventricular arrhythmia following repolarization disturbances. An important risk factor for repolarization disturbances is use of QT prolonging drugs. Drug interactions also play a clinically significant role in the development of QT prolongation substantially, in patients receiving multiple drugs that are prone to prolong QT interval. The present case study reports two consecutive QT prolongation events associated with drug-drug interactions in a patient during his hospital stay. A 63-years-old Asian male patient was being treated for his complaints of breathlessness, uneasiness, chest discomfort and palpitation (baseline QTc being 457 msec). During his treatment, he developed QT prolongation (QTc=494 msec) suspected to be the result of concomitant use of levofloxacin, ondansetron and ivabradine; QT prolongation reduced after discontinuation of the suspected drugs. In the same patient, during the later course of therapy, a concomitant use of amiodarone and fluconazole caused QT prolongation (QTc=536 msec) again. This complication is best approached with immediate discontinuation of interacting drugs and considering their alternatives. Knowledge enhancement regarding QT prolonging drug combinations and rapid ECG monitoring while using those combinations will help circumvent such adverse events.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".