Impact of Drug Induced Long QT Syndrome: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Drug induced long QT syndrome is quite common in daily clinical practice but its impact is unknown. METHODS: PubMed and EMBASE databases (until May 2, 2017) were searched to identify studies reporting drug induced long QT syndrome and followed the PRISMA guidelines. The main outcomes measured in these studies were QTc prolongation, ventricular arrhythmias, torsade de pointes (TdP) and death. RESULTS: Out of 176 non-duplicate reports, 36 studies satisfied inclusion criteria and provided data on patients exposed to drugs that can potentially cause long QT. Totally, 14,756 patients were exposed and 930 patients (6.3%) were found to have QTc prolongation. The number of males was 6,400 and females were 5,723 patients. The mean age of the patients was 43.8 ± 9.36 years. Ventricular arrhythmias were found in 379 patients (2.6%), 26 patients were found to have premature atrial contractions (PACs) and premature ventricular contractions (PVCs). TdP was found in 49 patients (0.33 %), sudden cardiac death (SCD) was found in five patients and 586 patients were found to have all-cause mortality. CONCLUSIONS: Around 6% of patients have risk of QT prolongation when exposed but only 0.3% developed TdP and 2.6% developed ventricular arrhythmias. Risk of developing arrhythmias is higher with concomitant use of multiple QT prolonging drugs.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".