The Effects of Cycle Ergometer Versus Treadmill Exercise Stress Testing on QTc Interval Prolongation in Patients With Long QT Syndrome: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The safest and most effective exercise stress tests (EST) modalities for long QT syndrome (LQTS) are currently unknown. The main objective was to explore the effects of EST on the corrected QT interval (QTc) in patients with LQTS, and to compare the effects of different EST modalities (cycle ergometer vs treadmill). DATA SOURCES: Systematic searches were performed in September 2022 in accordance with the PRISMA statement through PubMed, Medline, EBM Reviews, Embase, and Web of Science. MAIN RESULTS: A total of 1728 patients with LQTS, whether congenital or acquired, without any age restrictions (pediatric age ≤18 years and adult age >19 years), and 2437 control subjects were included in the 49 studies. The QT interval data were available for 15 studies. Our analyses showed that the QT interval prolonged in a similar manner using either a cycle ergometer or a treadmill (standardized mean difference [SMD] = 1.89 [95% CI, 1.07-2.71] vs SMD = 1.46 [95% CI, 0.78-2.14], respectively). Therefore, it seems that either modality may be used to evaluate patients with LQTS. CONCLUSIONS: The methodology for the measurement of the QT interval was very heterogeneous between studies, which inevitably influenced the quality of the analyses. Hence, researchers should proceed with caution when exploring and interpreting data in the field of exercise and LQTS.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.003 | 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".