Meta‐analysis of factors associated with antidiabetic drug prescribing for type 2 diabetes mellitus
Notice bibliographique
Résumé
BACKGROUND: There is a lack of consensus on prescribing alternatives to initial metformin therapy and intensification therapy for type 2 diabetes mellitus (T2DM) management. This review aimed to identify/quantify factors associated with prescribing of specific antidiabetic drug classes for T2DM. METHODS: Five databases (Medline/PubMed, Embase, Scopus, Web of Science) were searched using the synonyms of each concept (patients with T2DM, antidiabetic drugs and factors influencing prescribing) in both free text and Medical Subject Heading (MeSH) forms. Quantitative observational studies evaluating factors associated with antidiabetic prescribing of metformin, sulfonylurea, thiazolidinedione, Dipeptidyl-peptidase 4 inhibitors (DPP4-I), sodium glucose transporter 2 inhibitors (SGLT2-I), Glucagon-Like peptide receptor agonist (GLP1-RA) and insulin in outpatient settings and published from January 2009 to January 2021 were included. Quality assessment was performed using a Newcastle-Ottawa scale. The validation was done for 20% of identified studies. The pooled estimate was measured using a three-level random-effect meta-analysis model based on odds ratio [95% confidence interval]. Age, sex, body mass index (BMI), glycaemic control (HbA1c) and kidney-related problems were quantified. RESULTS: Of 2331 identified studies, 40 met the selection criteria. Of which, 36 and 31 studies included sex and age, respectively, while 20 studies examined baseline BMI, HbA1c and kidney-related problems. The majority of studies (77.5%, 31/40) were rated as good and despite that the overall heterogeneity for each studied factor was more than 75%, it is mostly related to within-study variance. Older age was significantly associated with higher sulfonylurea prescription (1.51 [1.29-1.76]), yet lower prescribing of metformin (0.70 [0.60-0.82]), SGLT2-I (0.57 [0.42-0.79]) and GLP1-RA (0.52 [0.40-0.69]); while higher baseline BMI showed opposite significant results (sulfonylurea: 0.76 [0.62-0.93], metformin: 1.22 [1.08-1.37], SGLT2-I: 1.88 [1.33-2.68], and GLP1-RA: 2.35 [1.54-3.59]). Both higher baseline HbA1c and having kidney-related problems were significantly associated with lower metformin prescription (0.74 [0.57-0.97], 0.39 [0.25-0.61]), but more insulin prescriptions (2.41 [1.87-3.10], 1.52 [1.10-2.10]). Also, DPP4-I prescriptions were higher for patients with kidney-related problems (1.37 [1.06-1.79]) yet lower among patients with higher HbA1c (0.82 [0.68-0.99]). Sex was significantly associated with GLP1-RA and thiazolidinedione prescribing (F:M; 1.38 [1.19-1.60] and 0.91 [0.84-0.98]). CONCLUSION: Several factors were identified as potential determinants of antidiabetic drug prescribing. The magnitude and significance of each factor differed by antidiabetic class. Patient's age and baseline BMI had the most significant association with the choice of four out of the seven studied antidiabetic drugs followed by the baseline HbA1c and kidney-related problems which had an impact on three studied antidiabetic drugs, whereas sex had the least impact on prescribing decision as it was associated with GLP1-RA and thiazolidinedione only.
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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 | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| 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 ».