Efficacy of ertugliflozin on hospitalisation for heart failure across the distribution of pre-trial ejection fraction: post hoc analyses of the VERTIS CV trial
Notice bibliographique
Résumé
Abstract Introduction There is controversy over whether SGLT2 inhibitors have efficacy in high-risk patients with heart failure (HF) and ejection fraction (EF) >60% with or without type 2 diabetes (T2D). The VERTIS CV trial studied a population of patients with T2D and atherosclerotic cardiovascular disease (ASCVD), 23% of whom had a history of HF. In VERTIS CV, ertugliflozin (ERTU) significantly reduced the risk of first and total hospitalisation for heart failure (HHF) vs placebo (PBO). Whether efficacy in the VERTIS CV population is consistent across the spectrum of pre-trial EF, particularly among those with EF >60%, is unknown. Purpose These post hoc analyses explored the effects of ERTU (5 mg; 15 mg) vs PBO on time to first and total HHF among patients in VERTIS CV across the spectrum of pre-trial EF. Methods As prospectively planned, the 2 ERTU dose groups were combined for all analyses vs PBO. Treatment effects of ERTU vs PBO on the risk of first and total HHF were analysed using adjusted Cox models for first and Andersen-Gill models for total (i.e., first + recurrent) events. Data on pre-trial EF were abstracted from the medical record at trial entry. Multiplicative interaction terms (EF × treatment arm) were used to determine if the efficacy of ERTU was modified by pre-trial EF. Results In VERTIS CV, 8246 patients were randomised to ERTU 5 or 15 mg or PBO (mean follow up 3.5 years). Overall, 5006 patients had pre-trial EF data available; 959 had EF ≤45%, 2860 had EF >45–60%, and 1187 had EF >60%. In the overall population, the event rate for first HHF was lower with ERTU vs PBO (hazard ratio [HR] 0.70; 95% CI 0.54–0.90). The findings were generally consistent across pre-trial EF (P-interaction = 0.26; Figure), including patients with pre-trial EF >60% (HR 0.72; 95% CI 0.34–1.55). In the overall population, event rate for total HHF was lower with ERTU vs PBO (HR 0.70; 95% CI 0.56–0.87). A significant interaction was observed between pre-trial EF and treatment arm for the risk of total HHF events (P-interaction = 0.02), with a greater magnitude of risk reduction in patients with a low pre-trial EF (≤45%; HR 0.39; 95% CI 0.26–0.57). However, the 95% CIs for the HR for total HHF for those with EF >45–60% and >60% nearly entirely or entirely contained the 95% CI of the overall population, respectively (Figure). Conclusion In the VERTIS CV trial of patients with T2D and ASCVD, the efficacy of ERTU in preventing first HHF was generally comparable across the spectrum of pre-trial EF. The trend for greater benefit at lower EF was statistically significant for total HHF events. Findings for patients with EF >45–60% and >60% appeared quantitively consistent with the overall findings for both first and total HHF. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): This study was sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, and Pfizer Inc., New York, NY, USA.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,010 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».