P5604Combining aortic size with measures of aortic stiffness and pulsatile hemodynamics enhances prediction of future thoracic aortic aneurysm expansion
Bibliographic record
Abstract
Abstract Introduction Thoracic aortic aneurysm (TAA) is a clinically silent disease which can lead to significant morbidity when complicated by an acute aortic syndrome. Although TAA size is the only variable used in decision-making, it is an imperfect predictor of risk. Conversely, hemodynamic measures that reflect the aorta's function, such as aortic stiffness and pulsatile hemodynamics, may provide additional insights into risk of TAA expansion. Purpose We hypothesized that combining aortic size with measures of arterial function (stiffness and pulsatile hemodynamics) would improve prediction of TAA expansion, as compared to aortic size alone. Methods 105 unoperated participants with TAA were recruited between 2014 and 2017 and followed prospectively for ≥1 yr. TAA size was measured at enrolment and at the latest imaging study according to published consensus; TAA expansion was calculated as mm/year. Arterial function was non-invasively assessed at baseline with validated methods that integrate arterial tonometry with echocardiography. Multivariable linear regression assessed independent associations of baseline TAA size and each arterial function measure, initially separately and then in combination (by multiplying them when direction of association was the same, and dividing them when direction of association was opposite), with future TAA expansion. Standardized beta coefficients were calculated to allow direct comparisons. Models were adjusted for age, sex, body size, aneurysm location and etiology, type of imaging modality, follow-up time, mean arterial pressure, and history of hypertension, diabetes and smoking. Results Seventy-seven percent of participants were men, and the ratio of degenerative to heritable TAAs was 62/43. Mean ± SD age, baseline TAA size, and follow-up time were 62.8±11.3yrs, 46.3±3.9cm, and 2.2±0.7 years, respectively. Results of the multivariable linear regression models are summarized in the Table. While baseline TAA size and each arterial function measure were independently associated with TAA expansion, some of the arterial function measures were superior in predicting TAA growth (Table, left). In addition, combining aortic size and function further improved the prediction of TAA growth beyond each variable alone (Table, right). Conclusion(s) Combining aortic size with arterial function improved prediction of TAA expansion over any individual variable alone, independently of confounders. Assessing arterial function may confer a clinical advantage, when compared to current practice, in determining TAA disease activity and estimating one's TAA-related risk. Acknowledgement/Funding Canadian Institute of Health Research, Canadian Vascular Network, and Heart and Stroke Foundation of Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".