Treatment persistence and adherence in people with type 2 diabetes switching to iGlarLixi vs free-dose combinations of basal insulin and glucagon-like peptide 1 receptor agonist
Notice bibliographique
Résumé
BACKGROUND: Fixed-ratio combinations of basal insulin (BI) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have greater simplicity of administration with expected improved adherence/persistence with therapy, but real-world data are lacking. OBJECTIVE: To compare medication persistence, adherence, and health care resource utilization (HRU) and costs for iGlarLixi (insulin glargine 100 U/mL and the GLP-1 RA lixisenatide) with newly initiated free-dose combinations of BI and a GLP-1 RA initiated simultaneously or sequentially. METHODS: This analysis used the US Optum Clinformatics (January 2017 to November 2019) database and included data from adults (aged ≥ 18 years) with type 2 diabetes and a glycated hemoglobin A1c (A1c) of 8% or more. Participants received iGlarLixi or free-dose combinations of BI and GLP-1 RAs prescribed simultaneously or subsequently. Participants were followed for 12 months. Cohorts were propensity score matched on baseline characteristics. The primary outcome was persistence (days on treatment without discontinuation). Secondary outcomes were adherence (proportion of days covered), change in A1c, and all-cause and diabetes-related HRU and costs. Subgroup analyses were performed for individuals with an A1c of 9% or more. RESULTS: After propensity score matching, there were 1,357 patients in each group; groups were well balanced. In the free-dose combination group, 65.6% started on BI, then added GLP-1 RAs; 28.5% started on GLP-1 RAs, then added BI; and 5.9% started on GLP-1 RAs and BI on the same day. In the subgroup with a baseline A1c level of 9% or more, 952 (iGlarLixi) and 932 (free-dose combination) participants were included. A significantly higher proportion of participants in the overall population who received iGlarLixi vs free-dose combinations were persistent (44.8% vs 36.3% [hazard ratio = 1.22, 95% CI = 1.11-1.35, P < 0.001]; the median [Q1, Q3] number of persistent days was 150 [63, 360] vs 120 [60, 310]) and adherent to therapy (41.3% vs 18.7% [odds ratio = 3.06, 95% CI = 2.57-3.65; P < 0.001]). Results for persistence in the subpopulation of participants with HbA1c levels of 9% or more were similar. Reductions in A1c from baseline were similar between iGlarLixi and the free-dose combination group (overall population: −1.2% vs −1.3%; P = 0.1913), but the number of participants in the database with follow-up A1c data was low. All-cause and diabetes-related pharmacy visits and total medication and diabetes medication pharmacy claims costs were significantly lower (all P < 0.001) for those receiving iGlarLixi vs free-dose combinations in both populations. CONCLUSIONS: In adults with type 2 diabetes, iGlarLixi was associated with longer persistence by approximately 30 days, improved adherence, and reductions in outpatient and pharmacy visits and in pharmacy costs. DISCLOSURES: This study was funded by Sanofi US. Medical writing support was provided by Barrie Anthony, PhD, CMPP, of Evidence Scientific Solutions and funded by Sanofi US. Dr Edelman has been on an advisory board and speakers’ bureau for AstraZeneca, MannKind, and Xeris and on an advisory board for BrightSight and is a board member for Senseonics and Team-Type1. Ms Cassarino is on the speakers’ bureau for Sanofi. Dr Kayne has been a consultant and speakers’ bureau member for AstraZeneca, Bayer, Dexcom, Eli Lilly & Company, Janssen, MannKind, Novo Nordisk, and Sanofi. Dr Dex and Mr Li are employees of Sanofi. Dr Pasquel has received unrestricted research support from Dexcom, Insulet, and Merck and has been a consultant for Medscape, AI Health, Boehringer Ingelheim, and Dexcom.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 ».