Assessing the effectiveness of adding gliclazide or pioglitazone in patients with type 2 diabetes using post-market observational data
Notice bibliographique
Résumé
Background: Both observational and experimental studies have shown substantial differences between pre-market evidence of efficacy and post-market evaluation of effectiveness, which highlights the need to evaluate both efficacy and effectiveness of new therapies. However, in diabetes, the direct comparison between effectiveness and efficacy on glycemic control is challenging given the non-systematic timing of the measurement of glycated hemoglobin (HbA1c) in real-life practice Objectives: To estimate the effectiveness and efficacy of adding pioglitazone or gliclazide to metformin in an adult population with type 2 diabetes using novel methods to estimate glycemic control and compare it to results obtained in an efficacy randomized controlled trial (RCT). The secondary aim is to examine the effectiveness of these medications in a subgroup of population who are usually excluded from efficacy trials, but in whom the medication is still prescribed: patients older than 75 years old. Methods: A retrospective cohort study was conducted using a large UK anonymised primary care research database, the Clinical Practice Research Datalink, to examine the effectiveness of pioglitazone and metformin compared with gliclazide and metformin. The population was selected to match the inclusion and exclusion criteria from a published RCT. HbA1c change between week 0 and 52, estimated using each patient's values during the follow-up by functional principal component analysis, was compared to the RCT results. Sensitivity analyses were conducted to assess the impact of limiting the analysis to patients whose medication dosage and adherence were similar to that achieved in the RCT. The same method was used to evaluate the comparative effectiveness in those over 75 years old who were excluded from the RCT.Results: The pioglitazone or gliclazide groups had a similar HbA1c change (pioglitazone -0.53%, 95%CI -0.69, -0.37 compared to gliclazide -0.46%, 95%CI -0.55, -0.36; difference between groups -0.08, 95% CI - 0.27, 0.10), which was less than the change observed in the RCT (-0.99% and -1.01% respectively). However, when limited to the subgroup of patients with equivalent medication dosage and adherence to that achieved in the RCT, our results approached those from the RCT: -1.11% (95%CI -1.52, -0.69,) and -0.69% (95%CI -0.97, -0.41) respectively. For patients over the age of 75, the addition of pioglitazone led to a change in HbA1c of -0.62% (95%CI -1.30, 0.07) compared to gliclazide -0.19% (95%CI -0.39, 0.00); difference between groups -0.24% (95%CI -0.73, 0.25).Conclusion: The addition of either pioglitazone or gliclazide to metformin resulted in similar reduction in the HbA1c by 0.5%, and approached results obtained in the RCT when restricted to patients with comparable adherence and medication dosage. Similar results were obtained for those over the age of 75, but were non-conclusive given the small sample size.
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 | 0,052 | 0,152 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,007 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».