Abstract P446: Prediction of Cardiovascular Events by Algorithm- and Formula-based Pulse Wave Velocity: An Analysis of CARTaGENE
Bibliographic record
Abstract
Background: Carotid-femoral pulse wave velocity (PWV) is a well-known marker of arterial stiffness and a cardiovascular risk factor. Novel estimations of PWV have been developed, but their ability to improve cardiovascular prediction made by clinical risk tools remains controversial. We hypothesized that PWV estimations can improve cardiovascular risk estimation beyong what is achievable from standard risk factors. Methods: Analysis of the population based CARTaGENE cohort, including participants aged between 40 and 69 years. PWV estimations were obtained using published formulas (fPWV) or algorithmic transformation of tonometry-based radial pulse waveforms captured at baseline (aPWV). 10-year cardiovascular risk for each participant was computed using the Atherosclerotic Cardiovascular Disease (ASCVD) and the SCORE-2 risk tools. All participants were passively followed during 10 years for major adverse cardiovascular events (MACE: cardiovascular death, stroke, myocardial infarction). Associations of fPWV and aPWV with MACEs were obtained using Cox models adjusted for ASCVD or SCORE-2 predictions. Results: 17,548 participants were eligible for the study (51.1% female, median age 53 yo, 8.9% diabetes, 13.9% prior cardiovascular disease), from which 2,263 (12.9%) experienced a MACE during follow-up. Mean PWV values at baseline were 8.4 ± 1.4 m/s (fPWV) and 7.9 ± 1.3 m/s (aPWV), and were closely correlated (correlation coefficient= 0.93). Both fPWV (HR= 1.52, 95% CI [1.47-1.58]) and aPWV (HR=1.60 [1.54-1.66]) were predictive of MACE in unadjusted models. Only aPWV remained significantly associated after adjustments for ASCVD (Hazard ratio [HR]= 1.16 [1.09-1.22]) and SCORE-2 (HR= 1.07 [1.00-1.13]). fPWV was significantly associated with MACE after adjustment for ASCVD, but not SCORE-2, only when this was tested in a population with similar exclusions as in the original fPWV derivation cohort. Conclusions: Algorithm-based PWV, but not formula-based PWV, improves cardiovascular prediction beyond what is achievable with recognized prediction tools.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 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".