Abstract 4142200: An Economic Evaluation of Non-HDL-Cholesterol and Apolipoprotein B as Treatment Targets for Lipid-Lowering Therapy in Primary Prevention
Bibliographic record
Abstract
Introduction: Apolipoprotein B (ApoB) is a better marker of residual risk for cardiovascular disease in patients treated with lipid-lowering therapy (LLT) than low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C). However, it is unclear if treating to an apoB target is more cost-effective than treating to an LDL-C or non-HDL-C target. Methods: We used the CVD Policy Model, a validated computer simulation model, to estimate the clinical and economic outcomes associated with atherogenic lipid targets for LLT in a cohort of statin-eligible and ASCVD-free U.S. adults. We considered non-HDL-C, and apoB targets for intensification of LLT. Treatments considered were intermediate-intensity statin therapy, high-intensity statin therapy, and ezetimibe, intensified in that order. Upon entering the model, all individuals commenced statin therapy. Under ‘usual care,’ patients with LDL-C ≥100 mg/dL after three months of treatment were escalated to higher-intensity treatment. Under non-HDL-C and apoB testing strategies, LLT was escalated if patients had non-HDL-C ≥119 mg/dL and apoB ≥78.7 mg/dL, respectively, based on percentile equivalence to the LDL-C target. The primary outcomes for our study were healthcare costs (2023 U.S. dollars) and quality-adjusted life years (QALYs). Secondary outcomes were CVD events prevented and life years gained. A lifetime horizon was adopted with a health sector perspective. Future costs and QALYs were discounted at 3% annually. Results: In a sex-balanced simulated cohort of 500,000 individuals, both non-HDL-C and apoB testing produced more QALYs and fewer costs than usual care (LDL-C target). Intensification based on apoB, produced 1,416 more QALYs than non-HDL-C-guided intensification, saving around $29,300,000 over the lifecourse of the simulated cohort. Compared to non-HDL-C testing, apoB testing would lead to 1,233 fewer CVD events and 3,800 more life years. Health gains were greater for men, though apoB screening was cost-saving (i.e., higher QALYs, lower cost) when compared to LDL-C and non-HDL-C testing for men and women. Conclusion: Making LLT intensification decisions based on apoB instead of LDL-C or non-HDL-C would save costs while improving population health.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".