7-006 The 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 equations offer a contemporary tool for estimating long-term cardiovascular (CV) risk in the general population.1 2 However, their prognostic significance in cancer survivors remains unclear. This study evaluates the association of baseline CV risk, calculated by PREVENT equations, with all-cause and CV mortality in cancer survivors. Methods Using 10 years of data from the National Health and Nutrition Examination Survey (NHANES) (2009–2018), we analysed a cohort representing over 18 million U.S. cancer survivors to evaluate the association between baseline CV risk, as defined by the PREVENT equations, and long-term all-cause and CV mortality. A Cox proportional hazards model was used to evaluate the relationship with all-cause mortality, while competing risk analysis was applied for CV mortality using the Fine and Gray semiparametric proportional hazards model, accounting for the competing risk of non-CV mortality. Models were adjusted for family income, education level, and cancer site. All statistical analyses were based on weighted records. Results A total of 18,722,334 weighted records (2,792 unweighted) were analysed, recording 4,875,627 all-cause deaths (26%) and 1,025,053 CV deaths (5.5%) over a 118-month median follow-up. When compared to low-risk individuals, those at high CV risk had a nearly sixteenfold higher risk of all-cause mortality (aHR: 15.60, 95% CI: 8.45–28.82, p < 0.001) and a fourteenfold higher risk of CV mortality (sHR: 14.01, 95% CI: 3.37–58.26, p < 0.001) during up to a decade of follow-up. Each 5% increase in baseline CV risk was associated with higher risks of all-cause mortality (36%) and CV mortality (50%) (aHR: 1.36 [95% CI: 1.30–1.42], sHR: 1.50 [95% CI: 1.35–1.66], p < 0.001 for both). Conclusion This study highlights the usefulness of the PREVENT score across diverse cancer survivor populations in predicting all-cause and CV mortality outcomes regardless of cancer site. References Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, et al. Novel prediction equations for absolute risk assessment of total cardiovascular disease incorporating cardiovascular-kidney-metabolic health: a scientific statement from the american heart association. Circulation [Internet]. 2023 Dec 12 [cited 2025 Mar 6];148(24):1982–2004. Available from: https://pubmed.ncbi.nlm.nih.gov/37947094/. Scheuermann B, Brown A, Colburn T, Hakeem H, Chen ,, Chow H, et al. External validation of the American heart association PREVENT cardiovascular disease risk equations key points + invited commentary + supplemental content. JAMA Netw Open. 2024;7(10):2438311.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".