Short-term beat-to-beat variability of the QT interval is increased and correlates with parameters of left ventricular hypertrophy in patients with hypertrophic cardiomyopathy
Bibliographic record
Abstract
Stratification models for the prediction of sudden cardiac death (SCD) are inappropriate in patients with hypertrophic cardiomyopathy (HCM). We investigated conventional electrocardiogram (ECG) repolarization parameters and the beat-to-beat short-term QT interval variability (QT-STV), a new parameter of proarrhythmic risk, in 37 patients with HCM (21 males, average age 48 ± 15 years). Resting ECGs were recorded for 5 min and the frequency corrected QT interval (QTc), QT dispersion (QTd), beat-to-beat short-term variability of QT interval (QT-STV), and the duration of terminal part of T waves (Tpeak-Tend) were calculated. While all repolarization parameters were significantly increased in patients with HCM compared with the controls (QTc, 488 ± 61 vs. 434 ± 23 ms, p < 0.0001; QT-STV, 4.5 ± 2 vs. 3.2 ± 1 ms, p = 0.0002; Tpeak-Tend duration, 107 ± 27 vs. 91 ± 10 ms, p = 0.0015; QTd, 47 ± 17 vs. 34 ± 9 ms, p = 0.0002), QT-STV had the highest relative increase (+41%). QT-STV also showed the best correlation with indices of left ventricular (LV) hypertrophy, i.e., maximal LV wall thickness normalized for body surface area (BSA; r = 0.461, p = 0.004) or LV mass (determined by cardiac magnetic resonance imaging) normalized for BSA (r = 0.455, p = 0.015). In summary, beat-to-beat QT-STV showed the most marked increase in patients with HCM and may represent a novel marker that merits further testing for increased SCD risk in HCM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".