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Enregistrement W2950880743 · doi:10.1097/01.hs9.0000560844.39623.aa

PF640 MULTIPLE MYELOMA TREATMENT LANDSCAPE FROM 2011 TO 2017 IN ALBERTA, CANADA: RESULTS FROM THE POPULATION‐BASED “IDENTIFYING OUTCOMES IN REAL‐WORLD MULTIPLE MYELOMA” (INFORMM) STUDY

2019· article· en· W2950880743 sur OpenAlexaffabout
Victor H. Jimenez‐Zepeda, G. Chen, Thomas E. Cowling, Eddie Shaw, Megan S. Farris, F.F. Liu, Jason Tay

Notice bibliographique

RevueHemaSphere · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of CalgaryAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaLenalidomideMedicinePopulationBortezomibAutologous stem-cell transplantationTransplantationFamily medicineInternal medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Background: The treatment landscape of multiple myeloma (MM) is rapidly evolving with the availability of new therapeutic options leading to improved responses and survival rates. The INFORMM study, an ongoing, province‐wide study in Alberta, Canada (population of 4.3 million in 2018), is examining treatment patterns in a real‐world setting using population‐based administrative data to better understand management of newly diagnosed multiple myeloma (NDMM) and outcomes in this era of novel therapies. Aims: Our goal was to analyze baseline characteristics and pre‐existing comorbidities, treatment patterns (including autologous stem cell transplantation [ASCT] and lines of therapy [LOT]), and treatment attrition rates in patients with NDMM, with or without ASCT. Methods: The NDMM population was derived using patient‐level data sources (Discharge Abstract Database, National Ambulatory Care Reporting System, and Practitioner Claims databases), and verified by clinical input from hematologists. Inclusion criteria were age ≥ 18 years, diagnosis of MM between April 2011 and March 2017, ≥ 1 LOT, and data available for a 1‐year period prior to the diagnosis date. Medication information was obtained from the Pharmaceutical Information Network database and patients receiving ASCT were identified using associated procedure codes from health services data sets. Treatment regimens were determined based on treatment availability during the study period, and classified as lenalidomide (LEN)‐based, bortezomib (BOR)‐based, LEN+BOR‐based, or other. Treatment lines were derived, based on a previously published algorithm for administrative data (Song et al. Curr Med Res Opin . 2016;32:95–103), and modified to align with MM treatment guidelines in Alberta. Results: Our study cohort consisted of 1,377 patients (828 men, 549 women). The mean (± standard deviation [SD]) age at diagnosis was 68.9 ± 12.2 years and mean (± SD) follow‐up time was 2.3 ± 1.6 years; 942 (68.4%) patients had a Charlson Comorbidity Index of ≥ 3 and 1,127 (81.8%) patients did not have diabetes at baseline. Overall, regardless of ASCT status, 45.8% (n = 630) of the 1,377 patients in the study cohort received more than one LOT, 47.3% (n = 298 or 21.6% of the overall cohort) went on to receive a third LOT, and 59.1% (n = 176 or 12.8% of the overall cohort) received additional LOTs. Within the first year of diagnosis, 328 (23.8%) patients underwent ASCT. Of these patients, 255 (77.7%) received ASCT as first‐line therapy; the remaining 73 (22.3%) patients received ASCT as second‐line therapy. Overall, higher attrition rates in subsequent LOTs were observed in the ASCT group compared with the non‐ASCT group (Table). Most patients had BOR‐based regimens in first‐line therapy, with increased use of LEN‐based and LEN+BOR‐based regimens observed in subsequent LOTs. Of patients undergoing ASCT (n = 328), 54.0% (n = 177) received maintenance therapy (LEN or BOR monotherapy), regardless of the baseline treatment regimen. Patients undergoing ASCT were younger compared with patients who did not receive ASCT (mean [± SD] age at MM diagnosis was 57.9 ± 7.3 vs 72.3 ± 11.4 years, respectively). Summary/Conclusion: To our knowledge, this is the first population‐based study utilizing administrative health data to examine the treatment landscape in an NDMM population in Alberta, Canada in the current era of novel therapies. High treatment attrition rates emphasize the importance of optimizing first‐line treatment opportunities. 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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,046
Tête enseignante GPT0,311
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission2
Résumé présentoui

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