Treatment Patterns and Associated Outcomes in Patients with Relapsed or Refractory Multiple Myeloma in the US and Non-US Countries: Findings from Preamble
Notice bibliographique
Résumé
Introduction: The management of multiple myeloma (MM) has undergone profound changes over the recent past due to newer therapeutic agents with novel mechanisms of action and refinement of treatment strategies. Proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and 3-drug regimens involving a PI and an IMiD (PI+IMiD) have become standards of care in MM. The immuno-oncology (I-O) agents elotuzumab and daratumumab were approved to treat relapsed or refractory (RR) MM by the US Food and Drug Administration in 2015, and by the European Medicines Agency in 2016. Extension of survival for patients with MM is a realistic goal, although access to therapies may vary between the US and non-US countries. Comparison of treatment patterns and associated outcomes for patients with RRMM in the US with non-US countries will shed light on the impact of treatment access on patient outcomes. Methods:Patients from the US and 5 non-US countries (Canada, France, Germany, Italy, and UK), ≥18 y of age with RRMM, ≥1 prior systemic therapy, and initiating treatment with a PI, an IMiD, a PI+IMiD combination, or an I-O agent within 90 d before or 30 d after study consent, were identified from PREAMBLE, an ongoing, prospective, non-interventional observational study (NCT01838512). Patient data were collected at each healthcare provider visit over a 3-y period or until the end of patient follow-up. Statistical comparisons were made using t tests and Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables; 2-sided p-values were obtained. Overall survival (OS) was analyzed using Cox regression and Kaplan-Meier techniques. Results: 1075 patients with RRMM (median age 69 y; 57.2% male) were enrolled; 341 (31.7%) from the US and 734 (68.3%) from non-US countries. At data cut-off (May 27, 2017), 522 (48.6%) patients had withdrawn from the study; 357 (68.4%) of patients who had withdrawn had died, mainly due to disease progression. Median follow-up was 18.7 mo (interquartile range: 8.8-31.5 mo). Patients from the US and non-US countries were similar with regard to sex, disease status, and International Staging System stage, although compared with patients from non-US countries, patients from the US were younger at baseline, had more comorbidities, and received more prior lines of treatment (Table). The proportion of patients receiving treatment in a subsequent line was similar between the US and non-US countries (54.3% vs 54.8%). Significantly more patients from the US received 2 or more subsequent therapies than patients from non-US countries (63.6% vs 53.4%; p=0.007). In addition, compared with patients from non-US countries, patients from the US were significantly more likely to receive I-O agents (8.2% vs 1.2%, p Median OS was longer for patients from the US than for patients from non-US countries (32.0 vs 28.6 mo), although the difference was not statistically significant (p=0.831). After adjusting for baseline differences between the US and non-US countries, mortality among patients from non-US countries was 30% higher than among patients from the US (hazard ratio 1.30; 95% CI 0.94-1.80; p=0.108). Conclusion: This pilot analysis of data from a clinical setting shows that patients with RRMM in the US had greater access to newer agents and received more subsequent therapies than patients in non-US countries. The longer OS in patients from the US may suggest clinical benefit associated with newer agents. Future analysis may be necessary to validate our findings. Study support:Bristol-Myers Squibb. Disclosures Vij: Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy; Takeda, Onyx: Research Funding. Chen: Bristol-Myers Squibb: Employment. Popov: Parexel: Employment. Durie: Johnson & Johnson: Consultancy; Takeda: Consultancy; Amgen: Consultancy. Cook: Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Glycomimetcs: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Zyczynski: Bristol-Myers Squibb: Employment. Davis: Bristol-Myers Squibb: Employment. Goldschmidt: Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity9s Board of Directors or advisory committees, Speakers Bureau; Chugai: Consultancy, Honoraria, Research Funding, Speakers Bureau; Morphosys: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Research Funding, Speakers Bureau.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».