Abstract 10361: Cardiovascular Risk Factor Control and Treatment Across the Spectrum of Patients with Atherosclerotic Cardiovascular Disease: The Precision Medicine Initiative - All of Us Study
Bibliographic record
Abstract
Objective: The 2018 Multisociety Cholesterol Guideline categorized patients with atherosclerotic cardiovascular disease (ASCVD) according to very high risk status, but there are few reports of recent medication use and risk factor control by risk group. We examined in a current cohort of US adults the extent of ASCVD risk factor control and treatment by these ASCVD groups. Methods: The NIH Precision Medicine Initiative (All of Us Study) is an ongoing program aiming to enroll > 1 million adults across the US. Since May 2018, >315,000 participants have been recruited from >340 sites nationwide, oversampling underrepresented groups. We studied adults age > 18 years with prior ASCVD, classified based on the 2018 guideline as 1) very high risk with > 2 major ASCVD events, 2) very high risk with 1 major event and > 2 major ASCVD conditions, or 3) ASCVD not at very high risk. We examined proportions at recommended therapies (high intensity statin, blood pressure [BP] and diabetes [DM] medication, and aspirin) and desired levels of risk factors (LDL-C, BP, HbA1c, and non-smoking status) across ASCVD risk categories. Results: Our 34,195 participants with ASCVD included 49% female, 20.5% non-Hispanic Black and 12.9% Hispanic or Latino adults, with an overall age of 66.0 + 12.4 years. 44.6% were classified as very high risk, of which 10.8% had > 2 prior ASCVD evets. The table shows the proportion of each risk group on recommended therapies and risk factor targets. Across ASCVD risk groups use of high intensity statins (10-14%) and attainment of acceptable LDL-C levels (17-27%) and BP levels (42-47%) are markedly suboptimal. Across risk groups only 3-8% were on all 4 recommended therapies and <5% were at all desired levels of all 4 risk factors. Conclusions: Improved efforts are needed to communicate the importance of multiple risk factor control and recommended therapies across the spectrum of ASCVD patients, and especially those at very high risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".