Real-World Treatment Patterns and Clinical Outcomes Among Triple-Class Exposed and B-Cell Maturation Antigen (BCMA) Exposed Multiple Myeloma Patients
Notice bibliographique
Résumé
Introduction: Despite the development of advanced treatment options, multiple myeloma (MM) remains an incurable disease. Most MM patients relapse and require further treatments. B-cell maturation antigen (BCMA) targeted immunotherapy is a novel MM therapy class that is now available to heavily pre-treated relapse/refractory MM (R/R MM) patients, who were triple class exposed (TCE) to proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and anti-CD38 monoclonal antibodies. As BCMA therapies continue to be further utilized and become an integral part of MM treatment, the proportion of TCE patients who are also BCMA exposed (TCE+BCMA exposed) is expected to grow. However, the clinical outcomes of TCE+BCMA exposed RRMM patients have not been previously assessed. The objective of this study was to evaluate the real-world outcomes of TCE+BCMA exposed MM patients who started a subsequent LOT, describing their characteristics, treatment patterns, and clinical outcomes. Subgroups of penta-drug exposed (at least two PIs, at least two IMiDs, and at least one anti-CD38) patients, as well as White and African American patients were also evaluated as penta-drug exposure status and race were known to impact clinical outcomes. Methods: Adult patients with MM who were TCE+BCMA exposed and started a subsequent LOT starting from July 03, 2019 (when additional new treatment options became available) were selected from the Komodo Healthcare Map (a US claims dataset) and were followed until the earliest of death, last claim, or end of study period (December 31, 2022). Index date was defined as the start date of the subsequent LOT following TCE and BCMA exposure. Descriptive statistics were provided for patient characteristics and treatment patterns. Clinical outcomes including time to treatment discontinuation or death (TTD) and time to next treatment or death (TTNT) were estimated using the Kaplan-Meier method. Results: A total of 344 TCE+BCMA exposed patients, who received the next LOT were included in the study, including 152 (44.2%) patients who were penta-drug exposed before index, 57 (16.6%) African American, and 185 (53.8%) White patients. Key baseline demographic characteristics and treatment history for the overall group and the three subgroups are listed in Table 1. The mean (SD) and median number of prior LOTs for the overall group before index date were 5.7 (1.7) and 6.0, respectively. The most prevalent prior treatments before the index date for the overall group were daratumumab (99.1%), belantamab mafadotin (91.9%), pomalidomide (84.3%), carfilzomib (83.1 %), bortezomib (76.2%) and lenalidomide (72.1%). Excluding corticosteroids, the most frequently used index treatments (i.e., treatment used post TCE+BCMA exposure) were carfilzomib- (21.9%), bortezomib- (20.8%), pomalidomide- (18.3%), and belantamab mafadotin- (18.3%) based regimens. The median (95% confidence interval [CI]) TTD was 3.7 (3.3, 4.4) months, and the median (95% CI) TTNT was 6.4 (5.5, 7.6) months. TTD and TTNT estimates for the three subgroups are listed in Table 2. Conclusions: To our knowledge, this is the first real-world study of treatment patterns and outcomes for TCE+BCMA exposed MM patients who started a subsequent LOT. The findings showed that this group of heavily pretreated MM patients were often retreated with the same therapies or therapies in the same classes, as they have limited effective treatment options. TCE+BCMA exposed patients exhibited poor clinical outcomes, as demonstrated by the rapid treatment discontinuation and moving on to the next treatment shortly after initial treatment. Many patients were already penta-drug exposed, and they appear to have worse clinical outcomes than the overall patient group. A shorter TTD was observed, and a trend for shorter TTNT was observed among African American patients than White patients. The findings highlight the continued need for effective treatments for heavily treated MM patients and particularly those with poor outcomes.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».