Characteristics of drugs for ultra-rare diseases versus drugs for other rare diseases in HTA submissions made to the CADTH CDR
Notice bibliographique
Résumé
BACKGROUND: It has been suggested that ultra-rare diseases should be recognized as distinct from more prevalent rare diseases, but how drugs developed to treat ultra-rare diseases (DURDs) might be distinguished from drugs for 'other' rare diseases (DORDs) is not clear. We compared the characteristics of DURDs to DORDs from a health technology assessment (HTA) perspective in submissions made to the CADTH Common Drug Review. We defined a DURD as a drug used to treat a disease with a prevalence ≤ 1 patient per 100,000 people, a DORD as a drug used to treat a disease with a prevalence > 1 and ≤ 50 patients per 100,000 people. We assessed differences in the level and quantity of evidence supporting each HTA submission, the molecular basis of treatment agents, annual treatment cost per patient, type of reimbursement recommendation made by CADTH, and reasons for negative recommendations. RESULTS: We analyzed 14 DURD and 46 DORD submissions made between 2004 and 2016. Compared to DORDs, DURDs were more likely to be biologic drugs (OR = 6.06, 95%CI 1.25 to 38.58), to have been studied in uncontrolled clinical trials (OR = 23.11, 95%CI 2.23 to 1207.19), and to have a higher annual treatment cost per patient (median difference = CAN$243,787.75, 95%CI CAN$83,396 to CAN$329,050). Also, submissions for DURDs were associated with a less robust evidence base versus DORDs, as DURD submissions were less likely to include data from at least one double-blinded randomized controlled trial (OR = 0.13, 95%CI 0.02 to 0.70) and have smaller patient cohorts in clinical trials (median difference = -108, 95%CI -234 to -50). Furthermore, DURDs are less likely to receive a positive reimbursement recommendation (OR = 0.22, 95%CI 0.05 to 0.91), and low level of evidence was the major contributor for a negative recommendation. CONCLUSIONS: The results suggest that DURDs could be viewed as distinct category from an HTA perspective. Applying the same HTA decision-making framework to DURDs and DORDs might have contributed the higher rate of negative reimbursement recommendations made for DURDs. Recognition of DURDs as a distinct subgroup of DRDs by explicitly defining DURDs based on objective criteria may facilitate the implementation of HTA assessment process that accounts for the issues associated with DURD.
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 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».