Predictive Value of Tpeak – Tend Indices for Adverse Outcomes in Acquired QT Prolongation: A Meta-Analysis
Bibliographic record
Abstract
Background: Acquired QT interval prolongation has been linked with malignant ventricular arrhythmias, such as torsade de pointes, in turn predisposing to sudden cardiac death. Increased dispersion of repolarization has been identified as a pro-arrhythmic factor and can be observed as longer Tpeak – Tend interval and higher Tpeak – Tend / QT ratio on the electrocardiogram. However, the values of these repolarization indices for predicting adverse outcomes in this context have not been systematically evaluated. Method: PubMed, Embase and Cochrane Library databases were searched until 14th February 2018, identifying 232 studies. Results: Five studies on acquired QT prolongation met the inclusion criteria and 308 subjects with drug-induced LQTS patients (mean age: 66 ± 18 years old; 46% male) were included in this meta-analysis. Tpeak – Tend intervals were longer (mean difference [MD]: 76 ms, standard error [SE]: 26 ms, P = 0.003; I2 = 98%) and Tpeak – Tend / QT ratios were higher (MD: 0.14, SE: 0.03, P = 0.000; I2 = 29%) in patients with torsade de pointes compared to those without these events. Conclusion: Tpeak – Tend interval and Tpeak – Tend / QT ratio were higher in patients with acquired QT prolongation suffering from torsade de pointes compared to those who did not. These repolarization indices may provide additional predictive value for identifying high-risk individuals.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".