Arterial Age and Early Vascular Aging, But Not Chronological Age, Are Associated With Faster Thoracic Aortic Aneurysm Growth
Bibliographic record
Abstract
Background Aneurysm size is an imperfect risk assessment tool for those with thoracic aortic aneurysm (TAA). Assessing arterial age may help TAA risk stratification, as it better reflects aortic health. We sought to evaluate arterial age as a predictor of faster TAA growth, independently of chronological age. Methods and Results We examined 137 patients with TAA. Arterial age was estimated according to validated equations, using patients' blood pressure and carotid‐femoral pulse wave velocity. Aneurysm growth was determined prospectively from available imaging studies. Multivariable linear regression assessed the association of chronological age and arterial age with TAA growth, and multivariable logistic regression assessed associations of chronological and arterial age with the presence of accelerated aneurysm growth (defined as growth>median in the sample). Mean±SD chronological and arterial ages were 62.2±11.3 and 54.2±24.5 years, respectively. Mean baseline TAA size and follow‐up time were 45.9±4.0 mm and 4.5±1.9 years, respectively. Median (interquartile range) TAA growth was 0.31 (0.14–0.52) mm/year. Older arterial age (ß±SE for 1 year: 0.004±0.001, P <0.0001) was independently associated with faster TAA growth, while chronological age was not ( P =0.083). In logistic regression, each 5‐year increase in arterial age was associated with a 23% increase in the odds of accelerated TAA growth (95% CI, 1.085–1.394; P =0.001). Conclusions Arterial age is independently associated with accelerated aneurysm expansion, while chronological age is not. Our results highlight that a noninvasive and inexpensive assessment of arterial age can potentially be useful for TAA risk stratification and disease monitoring as compared with the current clinical standard (chronological age).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".