Abstract 146: Evolving Clinical Benefits and Social Value of Lipid Lowering Therapies
Bibliographic record
Abstract
Background and Objectives: In 2013-14, approximately 30% of adults (20 years of age or older) in the United States (US) had high low-density lipoprotein cholesterol (LDL-C, LDL-C≥ 200 mg/dL). Elevated LDL-C is associated with an increased risk of cardiovascular disease (CVD) related events and death, and a key target for therapy. The lipid lowering treatment (LLT) landscape has evolved significantly over the course of the last 40 years with the introduction of statins, fibrates, ezetimibe, CETP inhibitors, and PCSK9 inhibitors (PCSK9i). Currently, statins are first-line therapy for LDL-C reduction. This study quantified the effect of LLT use since 1987 on CVD-related mortality and morbidity. Methods: Descriptive analyses of the rates of LLT use and LLT spending over time were conducted using both NHANES (1999-2014) and MEPS data (1996-2016), and the association between LDL-C levels and mortality and hospitalizations were identified in the literature. By using hazard ratios from published clinical trials, and NHANES data on the number of LLT users and frequency of non-fatal CVD events among these users, the total number of non-fatal CVD events prevented over time was calculated. Events avoided were calculated as the difference between CVD events observed among LLT users and events that would have occurred in the absence of LLT use. The latter was calculated using the difference in events between treated and untreated patients in LLT trials. The value associated with the prevention of non-fatal CVD events due to LLT use was calculated as the product of the costs associated with treating CVD events and the total number of CVD events prevented. The value of mortality reduction was calculated as the product of the value of a statistical life year ($150,000) and the life expectancy gain from the WOSCOPS and ODYSSEY clinical trials. This value was compared with LLT spending. Results: The uptake of statins and ezetimibe between 1999 and 2014 prevented an estimated total of 2.8M myocardial infarctions (MIs) and 1.7M strokes in the US. The cumulative value derived from MI and stroke events prevented through statin and ezetimibe use, though largely driven by statin use, from 1999–2012 amounted to approximately $258B (2018 USD) and $37B, respectively. The use of statins from 1999–2012 resulted in an additional 12.6 million life-years at a value of $1.9T, and we estimate that statin use could generate an additional $953.1B in value to society from 2018–2025 through reductions in CVD mortality, with patients retaining 94% of the value. Conclusions: Innovation in the LLT landscape, particularly through the introduction and uptake of statins, ezetimibe and PCSK9is, yields significant value to society through the reduction of costs associated with both fatal and non-fatal CVD events. Of the value that accrues to society from the utilization of statins, patients retain approximately 95%.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".