Prognostic Value of the American Heart Association PREVENT Cardiovascular Disease Risk Equations in Cancer Survivorship: A NHANES Population‐Based Study (2009–2018)
Bibliographic record
Abstract
Background The PREVENT (Predicting Risk of CVD Events) equations offer a contemporary tool for estimating long‐term cardiovascular risk in the general population. This study evaluates the association of baseline cardiovascular risk, calculated by PREVENT equations, with all‐cause and cardiovascular mortality in cancer survivors. Methods Using 10 years of data from the National Health and Nutrition Examination Survey (NHANES) (2009–2018), we analyzed a nationally representative cohort of US cancer survivors. Associations with outcomes were evaluated using Kaplan–Meier curves and multivariable Cox models. Results A total of 18 722 334 weighted records (2792 unweighted) were analyzed, recording 4 875 627 all‐cause deaths (26%) and 1 025 053 cardiovascular deaths (5.5%) over a median follow‐up of 9.8 years; 27.84% of cancer survivors were at high baseline cardiovascular risk with variability in baseline cardiovascular risk across different cancer sites. Colon and prostate cancer survivors had the highest prevalence of high cardiovascular risk (54% and 46%, respectively). When compared with low‐risk individuals, those at high cardiovascular risk had a nearly 16‐fold higher risk of all‐cause mortality (adjusted hazard ratio, 15.60 [95% CI, 8.45–28.82]; P <0.001) and a 13‐fold higher risk of cardiovascular mortality (adjusted hazard ratio, 12.71 [95% CI, 3.00–53.73]; P <0.001) up to a decade of follow‐up. Each 5% increase in baseline cardiovascular risk was associated with higher risks of all‐cause mortality (36%) and cardiovascular mortality (51%) (adjusted hazard ratio, 1.36 [95% CI, 1.30–1.42]; adjusted hazard ratio, 1.51 [95% CI, 1.33–1.72], P <0.001 for both). Conclusions This study highlights the usefulness of the PREVENT equations for predicting all‐cause and cardiovascular mortality in cancer survivors.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".