Aorta Wall Stress during Exercise in Patients with an Ascending Thoracic Aortic Aneurysm: Insights from a Case Series
Bibliographic record
Abstract
Abstract Individuals with ascending thoracic aortic aneurysm (ATAA) are recommended to avoid intense exercise for fear of marked increases in aortic wall stress (AWS). However, no study has measured AWS during exercise. The aim of this case series was to examine AWS during “light-to-moderate” aerobic exercise in individuals with ATAA and healthy control (CON) participants. Three clinically stable patients with ATAA (2 male, mean age: 74 ± 1 years) and 3 CON (2 male, mean age: 69 ± 7 years) were studied on 2 separate days. Day 1: a maximal cardiopulmonary exercise test was performed to measure peak aerobic power (VO2peak), maximal heart rate, and blood pressure (BP). Day 2: cardiac and aortic magnetic resonance imaging were performed at rest and during submaximal (3–5 metabolic equivalents) “stepper” exercise during which cardiac output (Qc), aorta diameters, wall thickness, and BP were measured. Circumferential ascending and descending AWS were calculated in accord with LaPlace Law, whereas aorta mechanical efficiency was derived as the AWS/Qc slope. Patients with ATAA demonstrated lower median VO2peak (18.2 vs. 24.1 mL/kg/min). During exercise, the absolute ascending (ATAA: 257 vs. CON: 269 kPa) and descending AWS increased (ATAA: 224 vs. CON: 207 kPa), and ∆AWS during exercise was similar between ATAA and CON (Ascending, ATAA: 79 vs. CON: 62 kPa; Descending, ATAA: 64 vs. CON: 55 kPa). During exercise, ascending and descending AWS were 76 to 83% below ATAA rupture thresholds (i.e., 800–1,200 kPa) in all patients. Finally, exercise Qc was 17% lower and the ascending AWS/Qc slope was 30% higher in ATAA (16 kPa/L/min) versus CON (12 kPa/L/min). Our findings demonstrate “light-to-moderate” aerobic exercise produces similar AWS responses between ATAA and CON and is well below aneurysmal rupture thresholds. The higher AWS/Qc slope in ATAA suggests decreased aortic mechanical efficiency and may be a useful measure for exercise prescription for these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".