The impact of high‐intensity interval training on ventricular remodeling in patients with a recent acute myocardial infarction—A randomized training intervention pilot study
Bibliographic record
Abstract
Abstract Background Aerobic exercise training is associated with beneficial ventricular remodeling and an improvement in cardiac biomarkers in chronic stable heart failure. High‐intensity interval training (HIIT) is a time‐efficient method to improve in stable coronary heart disease patients. This pilot study aimed to compare the effect of HIIT on ventricular remodeling in patients with a recent acute myocardial infarction (AMI). Methods Nineteen post‐AMI patients were randomized to either HIIT (n = 9) or usual care (n = 10). A cardiopulmonary exercise test (CPET), transthoracic echocardiography, and cardiac biomarker assessment (ie, N‐terminal pro B‐type natriuretic peptide levels and G protein‐coupled receptor kinase 2 expression) were performed before and after a 12‐week training intervention. CPET parameters including oxygen uptake efficiency slope (OUES) and at the first ventilatory threshold ( VT1) were calculated. left ventricular (LV) structural and functional echocardiographic parameters including myocardial strain imaging were assessed. Results and OUES improved solely in the HIIT group (P < .05 for group/time, respectively). There was a significant training effect for the improvement of peak work load in both groups (P < .05). O2 pulse and at VT1 both improved only in the HIIT group (P < .05 for time, no interaction). HIIT improved radial strain and pulsed‐wave tissue Doppler imaging derived e′ (P < .05 for time, no interaction). Cardiac biomarkers did not change in either group. Conclusions In post‐AMI patients, HIIT lead to significant improvements in prognostic CPET parameters compared to usual care. HIIT was associated with favorable ventricular remodeling regarding certain echocardiographic parameters of LV function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".