PS1376 LENALIDOMIDE MAINTENANCE POSITIVELY IMPACTS OUTCOMES IN MULTIPLE MYELOMA WITHOUT NEGATIVE IMPACTS IN RELAPSE: AN ANALYSIS OF REAL WORLD DATA FROM THE MYELOMA CANADA RESEARCH NETWORK NATIONAL DATABASE
Notice bibliographique
Résumé
Background: Randomized trials have demonstrated the positive impact of lenalidomide maintenance (LM) on outcomes following autologous stem cell transplant (ASCT) in multiple myeloma (MM). This finding has led to its wide‐spread adoption as a standard of care. However, data examining its effect in the real‐world setting is lacking. Aims: Using the Myeloma Canada Research Network Canadian Multiple Myeloma Database (MCRN CMM‐DB) we have examined the impact of LM at a national level. Methods: We retrospectively reviewed data from patients tracked using the MCRN CMM‐DB. This web‐based centralized platform characterizes real‐world outcomes in patients treated at 13 major Canadian academic institutionsand includes comprehensive data on >6000 patients dating back to 2007 with ongoing prospective data collection. This analysis examined patients treated with bortezomib‐based induction therapy prior to ASCT. Subjects were included up until January 2016 to ensure at least 2 years of follow‐up. The analysis was performed based on intention‐to‐treat with LM. Overall survival (OS) was defined as time from pre‐ASCT induction therapy to death or last follow‐up (F/U). Progression‐free survival (PFS) was defined as the time from induction therapy to progression, death or last F/U. We also calculated outcomes of relapsing patients in both arms, including 2nd PFS (from second‐line therapy to second relapse, death or last F/U) and PFS2 (time from induction therapy to second relapse, death or last F/U). Lastly, given regional variation in LM dosing schedules, we examined outcomes based on a 21/28 days or continuous 28/28 days dosing strategy. Results: Data from 1,256 patients across 10 Canadian centers was included (723 with LM and 533 without). Median follow‐up in the LM group was 49 months and 45 months in the non‐LM group. The median OS was not reached in the LM cohort and 98 months in the non‐LM group (p <0.0001, figure 1a). The median PFS also favoured patients treated with LM (58 months versus 35 months respectively, p <0.0001, figure 1b). Response rates were high with 98% achieving ≥PR in the LM group compared to 96% in the non‐LM group (p = 0.06). In LM patients 94% achieved ≥VGPR compared to 81% in the non‐LM group (p <0.01). At the time of analysis, 45% of LM and 63% of non‐LM patients have relapsed. Treatment for relapsed disease resulted in a similar median 2nd PFS between LM vs non‐LM patients (12 months vs 16 months respectively, p = 0.07, figure 1c). The median PFS2 remained in favour of LM compared with non‐LM (not reached vs 68 months, p < 0.01, figure 1d). Median duration of maintenance therapy was 20.5 months (0.0 ‐ 102.48+). Analysis of patients treated with a 28/28 days (n = 356) versus a 21/28 (n = 257) days LM dosing schedule showed no difference in estimated 5‐year OS (81% and 80% respectively, p = 0.66) or 5‐year PFS (47% and 52% respectively, p = 0.75). Summary/Conclusion: Using the MCRN CMM‐DB we present one of the largest real‐world cohorts demonstrating the dramatic impact on outcomes using LM following bortezomib‐based induction. Notable improvements are seen in PFS, OS and depth of response. The truncated 21/28 day dosing strategy did not negatively impact these endpoints. Importantly, LM did not demonstrate a negative effect on the outcomes with second‐line therapy, as the median 2nd PFS was similar and the PFS2 remained in favour of LM. Overall, this large real‐world cohort supports the ongoing use of LM in the frontline management of MM. image
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 | 0,004 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».