Aortic Stiffness, Central Blood Pressure, and Pulsatile Arterial Load Predict Future Thoracic Aortic Aneurysm Expansion
Bibliographic record
Abstract
Thoracic aortic aneurysm is a disease associated with high morbidity and mortality. Clinically useful strategies for medical management of thoracic aortic aneurysm are critically needed. To address this need, we sought to determine the role of aortic stiffness and pulsatile arterial load on future aneurysm expansion. One hundred five consecutive, unoperated subjects with thoracic aortic aneurysm were recruited and prospectively followed. By combining arterial tonometry with echocardiography, we estimated measures of aortic stiffness, central blood pressure, steady, and pulsatile arterial load at baseline. Aneurysm size was measured at baseline and follow-up with imaging; growth was calculated in mm/y. Stepwise multivariable linear regression assessed associations of arterial stiffness and load measures with aneurysm growth after adjusting for potential confounders. Mean±SD age, baseline aneurysm size, and follow-up time were 62.6±11.4 years, 46.24±3.84 mm, and 2.92±1.01 years, respectively. Aneurysm growth rate was 0.43±0.37 mm/y. After correcting for multiple comparisons, higher central systolic (β±SE: 0.026±0.009, P =0.007), and pulse pressures (β±SE: 0.032±0.009, P =0.0002), carotid-femoral pulse wave velocity (β±SE: 0.032±0.011, P =0.005), amplitudes of the forward (β±SE: 0.044±0.012, P =0.0003) and reflected (β±SE: 0.060±0.020, P =0.003) pressure waves, and lower total arterial compliance (β±SE: −0.086±0.032, P =0.009) were independently associated with future aneurysm growth. Measures of aortic stiffness and pulsatile hemodynamics are independently associated with future thoracic aortic aneurysm growth and provide novel insights into disease activity. Our findings highlight the role of central hemodynamic assessment to tailor novel risk assessment and therapeutic strategies to patients with thoracic aortic aneurysm.
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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 | 0.000 | 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 | 0.001 |
| 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".