Cardiovascular Risk Prediction Improvement Using Algorithm- or Formula-Based Pulse Wave Velocity: Analysis of CARTaGENE
Bibliographic record
Abstract
Background: Carotid-femoral pulse wave velocity (PWV) is the gold-standard measurement for aortic stiffness and a well-established surrogate marker for cardiovascular disease. Faster and less resource-intensive methods to estimate PWV (using either formulas or integrated pulse wave analysis algorithms) have been developed but their incremental predictive value for cardiovascular outcomes remains unclear. Methods: We studied individuals aged between 40 and 69 from the population based CARTaGENE cohort (Quebec, Canada). Baseline PWV was assessed using a previously described estimation formula (formula- based PWV, or f-PWV; using age, sex, and systolic blood pressure) or estimated with the ARCSolver algorithm from central waveform characteristics obtained with the SphygmoCor device (algorithm-based PWV, or a-PWV). Major adverse cardiovascular events (MACE: cardiovascular mortality, non- fatal stroke, non-fatal myocardial infarction) during a 10-year follow-up were obtained from medico-administrative databases. Cox proportional hazards models were employed to obtain associations between PWV and MACE after adjustment for existing cardiovascular risk prediction scores (ASCVD [from revised pooled cohort equations], SCORE-2). Results: 17,548 individuals were included and 2,263 experienced a MACE during follow-up. Mean PWV values at baseline were 8.4 ± 1.4 m/s (f-PWV) and 7.9 ± 1.3 m/s (a-PWV). Both f-PWV (HR= 1.52, 95% CI [1.47-1.58]) and a-PWV (HR=1.60 [1.54-1.66]) were predictive of MACE in unadjusted models. The association between a-PWV and MACE remained significant after adjustment for ASCVD (HR= 1.14 [1.08-1.20]) and but not after adjustment for SCORE-2 (HR= 1.06 [1.00-1.13]). In contrast, f-PWV was not associated with increased MACE after adjustment for either prediction score (HR= 1.02 [0.97-1.08] for ASCVD; HR= 0.95 [0.89-1.00] for SCORE-2). Similar trends were observed after stratification for tertiles of baseline cardiovascular risk. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".