Subclinical atherosclerosis, cardiovascular health, and disease risk: is there a case for the Cardiovascular Health Index in the primary prevention population?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Current primary prevention guidelines for cardiovascular disease (CVD) prioritize risk identification, risk stratification using clinical and risk scores, and risk reduction with lifestyle interventions and pharmacotherapy. Subclinical atherosclerosis is an early indicator of atherosclerotic burden and its timely recognition can slow or prevent progression to CVD. Thus, individuals with subclinical atherosclerosis are a priority for primary prevention. This study takes a practical approach to answering a challenge commonly faced by primary care practitioners: in patients with no known CVD, how can individuals likely to have subclinical atherosclerosis be easily identified using existing clinical data and/or information provided by the patient? METHODS: Using NHANES (1999-2004), 6091 men and women aged ≥40 years without any CVD comprised the primary prevention population for this study. Subclinical atherosclerosis was determined via ankle-brachial index (ABI) using established cutoffs (subclinical atherosclerosis defined as ABI (0.91-0.99); normal defined as ABI (1.00-1.30)). Three common scores were calculated: the Framingham Risk Score (FRS), the Metabolic Syndrome (MetS), and the Cardiovascular Health Index (CVHI). Logistic regression analysis assessed the association between these scores and subclinical atherosclerosis. The sensitively and specificity of these scores in identifying subclinical atherosclerosis was determined. RESULTS: In eligible participants, 3.8% had subclinical atherosclerosis. Optimum and average CVHI was associated with decreased odds for subclinical atherosclerosis. High, but not intermediate-risk, FRS was associated with increased odds for subclinical atherosclerosis. MetS was not associated with subclinical atherosclerosis. Of the 3 scores, CVHI was the most sensitive in identifying subclinical atherosclerosis and had the lowest number of missed cases. The FRS was the most specific but least sensitive of the 3 scores, and had almost 10-fold more missed cases vs. the CVHI. The MetS had "middle" sensitivity and specificity, and 10-fold more missed cases vs. the CVHI. CONCLUSIONS: Results from this study suggest that routine administration of the CVHI in a primary prevention population would yield the benefits of identifying patients with existing subclinical CVD not identified through traditional CVD risk factors or scores, and bring physical activity and nutrition to the forefront of provider-patient discussions about lifestyle factors critical to maintaining and prolonging cardiovascular health.
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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it