A Point-of-Care Interactive Decision Tool Reveals Variance Between Clinicians and Experts in Selecting Among GLP-1 RAs in T2D
Notice bibliographique
Résumé
Abstract Background: T2D management is shifting toward treating patients with therapies that align with their level of CV and end-organ risk. To this end, evidence-based guidelines now recommend glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for both glycemic and extraglycemic benefits. The great speed with which these recommendations change create immediate gaps in knowledge and competence, especially as they relate to managing patients with comorbid CV and/or renal disease. To help clinicians understand GLP-1 RA therapies and their novel characteristics in practice, we developed a decision support tool where choice of treatment among GLP-1 RAs is guided by a panel of experts. Methods: We developed a decision support tool with guidance from 5 experts who provided therapy recommendations for 48 unique patient case scenarios based on patient variables including CVD, CKD, retinopathy, A1C level, and need for weight loss. Clinician learners are prompted to specify a patient scenario using these variables before selecting an intended therapy. After all questions are completed for a patient scenario, the tool displays what the panel of experts recommend and then asks the learner if this information changed their intended choice. Results: From February through October 2020, 983 learners entered 1433 unique patient case scenarios. Of these, 365 were anonymous and 623 were authenticated, of which 70% (n = 437) were from the US; 50% (n = 310) were MDs; 22% (n = 135) were nurses, NPs, or PAs; and 19% (n = 121) were PharmDs. The intended therapy of learners differed from the experts in 34% (n = 489) of cases and were limited to 3 categories: cases in which learners chose to use exenatide (17%), cases in which they chose to use a GLP-1 RA in conjunction with insulin (12%), or cases in which they were unsure (71%). Of note, of the 93 cases in which learners chose exenatide, 68% (n = 63) were cases with CVD and/or CKD, where exenatide was not recommended by experts. Similarly, of the 89 cases in which learners chose insulin with a GLP-1 RA, 57% (n = 51) were cases with A1C < 9%, where insulin was not recommended by experts. Of cases in which learners’ intended therapy differed from the experts’ (and they indicated the impact of the tool), 52% indicated that they planned to change their treatment plan. Conclusion: This tool highlights continuing gaps in clinicians’ ability to select among GLP-1 RAs for T2D. Using a decision support tool can positively influence practice behaviors: Learners can see if their intended treatment choice is congruent with a panel of experts and change plans as appropriate.
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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 | 0,017 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,005 |
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 ».