Notice bibliographique
Résumé
Multiple Myeloma: Multiple MyelomaOutcomes for multiple myeloma patients treated with standard regimens fall far shorter in the real world than in randomized clinical trials (RCTs), according to a new study. People treated for multiple myeloma in real-world hospital settings experienced a 44 percent higher rate of disease progression or death and a 75 percent higher rate of death than was reported in clinical trials for six of the seven common myeloma treatments. Serious adverse event (SAE) rates were comparable among clinical trial participants and real-world patients. “This is one the largest population-level studies highlighting the significant efficacy-effectiveness gap with multiple standard-of-care regimens for multiple myeloma. It underscores the importance of understanding whether RCT outcomes are generalizable to our real-world patients,” said lead author Alissa Visram, MD, MPH, from the Division of Hematology at The Ottawa Hospital Research Institute in Canada, at the 65th ASH Annual Meeting & Exposition. RCTs are the gold standard for regulatory approval and treatment guidelines and informing patients about expected outcomes. Visram and colleagues set out to find how much the RCT efficacy differs from the real-world effectiveness for multiple myeloma therapies. Many real-world patients would not have met the stringent RCT inclusion criteria, she noted. Study Details At ASH, Visram presented the results of a retrospective population-based study (Abstract 541) to compare the efficacy versus effectiveness of registration RCTs with real-world patients using seven standard-of-care regimens for multiple myeloma. These regimens included lenalidomide/dexamethasone (Rd) and bortezomib/Rd (VRd) for newly diagnosed, transplant-ineligible patients and carfilzomib/Rd (KRd), carfilzomib/dexamethasone (Kd), daratumumab/Rd (DRd), daratumumab/bortezomib/dexamethasone (DVd), and pomalidomide/dexamethasone (Pd) for relapsed/refractory patients. At a health care system in Ontario, Canada, information was received from the Institute for Clinical Evaluative Sciences from a database capturing all health records. A total of 3,951 real-world multiple myeloma patients (1,106 patients with newly diagnosed transplant-ineligible disease and 2,845 patients with relapsed disease) treated between January 2007 to December 2020 with these standard-of-care regimens were included. Patients in the real-world cohort were older than in the RCTs, Visram noted. For relapsed regimens, there was a longer time between multiple myeloma diagnosis and the start of the regimen in the real-world versus RCTs. The data revealed a large difference in real-world versus RCT outcomes. A meta-analysis showed that, across all regimens and disease settings, the real-world patients had worse progression-free survival (PFS) (HR: 1.44) and overall survival (OS) (HR: 1.75). PFS estimates showed absolute differences reaching as high as 18.3 months for patients treated with KRd versus 26.3 months for the RCT group. OS differences for six of the seven regimens evaluated favored the RCT patients by more than 19 months. Crude estimates showed that RCT participants on Kd lived a median 37.9 months longer compared with the real-world patients. Those treated with VRd in RCTs lived at least 35.9 months longer and those on KRd in trials lived 26.7 months longer. Pd was the only regimen that performed as well as or slightly better in the real-world setting than in clinical trials. The reason for this is likely multifactorial, but Visram suggested that perhaps patients included in the Pd RCT may have had more refractory multiple myeloma, given the higher prior immunomodulatory drug exposure and longer time from diagnosis to treatment among patients compared to real-world patients in the study. With regards to safety, the percentage of patients with inpatient hospitalization during treatment in the real-world cohort and reported SAEs in RCT were comparable: VRD 57 percent versus not reported; Rd 64 percent versus not reported; Kd 57 percent versus 59 percent; KRd 53 percent versus 60 percent; DVd 36 percent versus not reported; DRd 46 percent versus 49 percent; and Pd 59 percent versus 61 percent. “Our results will better inform both clinicians and patients to allow for shared treatment decision-making,” Visram noted. The analysis also underscores the limitations of clinical trials in predicting outcomes among patient populations that are typically different from those in clinical trials in terms of demographics, health status, and care settings, such as community practices versus academic medical centers. “The criteria for clinical trial eligibility are often quite stringent, so the results are not always generalizable,” Visram said. “It's not a surprise that real-world patients don't do as well as those in clinical trials, but our study is the first to quantify the difference. It suggests we need to change our frame of reference and better contextualize what outcomes we would expect our patients to have.” Mark L. Fuerst is a contributing writer.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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) | 0,001 | 0,001 |
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 ».