Replicating cardiovascular outcome trials for type 2 diabetes using real-world evidence: protocol for a systematic review of observational studies.
Notice bibliographique
Résumé
Randomized controlled trials (RCTs) are the current gold standard for drug safety and efficacy evidence-based regulatory decision making. However, due to their strict inclusion and exclusion criteria trial populations may not be generalizable to the real-world population of interest. (2). RCTS are also resource and time intensive. With the adoption of electronic health records, large amounts of real-world evidence (RWE) on drug exposure and health outcomes are becoming readily available. This is seen as an attractive alternative to evaluate the effectiveness and safety of medical interventions. The US Food and Drug Administration (FDA) and Health Canada are adapting guidelines to incorporate real world evidence in their decision-making. Health Canada has launched an initiative to integrate RWE throughout the life cycle of drugs (3). The FDA is evaulating the potential role of observational studies in contributing to evidence of drug effectiveness (4). They highlight the need to replicate RCTs using rigorously designed observational studies for insight into the opportunities and limitations of using RWE in regulatory decision making(4). In 2008, the FDA issued recommendations that cardiovascular safety trials should be conducted to prove that antidiabetic medications have acceptable cardiovascular risk profiles (5). These recommendations were made in the wake of concerns over increased risk of cardiovascular events from the antidiabetic medication, rosiglitazone, for patients with type 2 diabetes (6). Since then, more than 13 cardiovascular outcome trials have been conducted on antidiabetic drugs for type 2 diabetes including, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide 1 (GLP-1) receptor agonists, and sodium glucose cotransporter-2 (SGLT-2) inhibitors. Broadly speaking, results from these trials demonstrated cardiovascular safety (7). However, these trials were highly selective and may not be generalizable to the larger population. Many of the trials required the presence of high cardiovascular risk factors to increase number of events. Evidence has shown that using the UK’s Royal College of General Practitioners Research and Surveillance Centre database, only 16% of adults with type 2 diabetes had the same high cardiovascular risk factors as those included in the EMPA-REG trial which evaluated SGLT-2 inhibitors (8). For those already initiated on SGLT-2 inhibitors only 11% had a similar risk profile as those included in the trial (8). When the DUPLICATE team emulated cardiovascular outcome trials using US commercial and Medicare patient-level claims data for antidiabetic and antiplatelet medications, they found that only 60% of trials had concordant regulatory conclusions (9). Given that the FDA is striving to understand the complementary nature of RWE to RCTs, coupled with the growing body of evidence on cardiovascular outcome trials in type 2 diabetes and their replication using non-randomized data, we hope to synthesize the information in this area to understand what proportion of RWE patients are eligible for cardiovascular outcome trials, and when restricted to cardiovascular outcome trial eligibility, how patient characteristics and outcomes compare to their respective cardiovascular outcome trials.
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 | 0,138 | 0,195 |
| Méta-épidémiologie (sens strict) | 0,009 | 0,006 |
| Méta-épidémiologie (sens large) | 0,026 | 0,025 |
| Bibliométrie | 0,014 | 0,018 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,007 | 0,007 |
| Intégrité de la recherche | 0,010 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,011 |
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 ».