Prolonged T <sub>peak</sub> ‐T <sub>end</sub> interval is a risk factor for sudden cardiac death in adults with congenital heart disease
Bibliographic record
Abstract
OBJECTIVE: (TpTe) interval, a measure of dispersion of ventricular repolarization, is a risk factor for SCD in non-ACHD patients. We aim to evaluate whether TpTe can be used in risk stratification for SCD in ACHD patients. DESIGN: From an international multicenter cohort of 25 790 ACHD patients, we identified all SCD cases. Cases were matched to controls by age, gender, congenital defect, and (surgical) intervention. OUTCOME MEASURES: TpTe was measured on a standard 12-lead ECG. The maximum TpTe of all ECG leads (TpTe-max), mean (TpTe-mean), and TpTe dispersion (maximum minus minimum) were obtained. Odds ratios (OR) for SCD cases vs controls were calculated using conditional logistic regression analysis. RESULTS: ECGs were available for 147 cases (median age at death 33.5 years (quartiles 26.2, 48.7), 66% male) and 267 controls. The mean TpTe-max was 97 ± 24 ms in cases vs 84 ± 17 ms in controls (P < .001); TpTe-mean was 70 ± 16 vs 63 ± 10 ms (P < .001); and dispersion was 51 ± 22 ms vs 41 ± 16 ms (P = .02), respectively. Assessing each ECG lead separately, TpTe in lead aVR predicted SCD most accurately. TpTe in lead aVR was 71 ± 23 ms in cases vs 61 ± 13 ms in controls (P < .001). After adjusting for impaired ventricular function, heart failure symptoms, and prolonged QRS duration, the OR of SCD of TpTe in lead aVR at an optimal cutoff of 80 ms was 5.8 (95% CI 2.7-12.4, P < .001). CONCLUSIONS: The TpTe interval is associated with SCD in ACHD patients. Particularly, TpTe in lead aVR can be used as an independent risk factor for SCD in ACHD patients and may, therefore, add precision to current risk prediction models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".