Abstract 146: Evolving Clinical Benefits and Social Value of Lipid Lowering Therapies
Notice bibliographique
Résumé
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%.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».