A Real-World Evidence Study of the Temporal Evolution in Treatment Patterns and Outcomes for Chronic Lymphocytic Leukemia
Notice bibliographique
Résumé
CONCLUSIONS Chronic lymphocytic leukemia (CLL) is the most common lymphoproliferative disorder in North America. Treatment of CLL is currently indicated only for patients with disease-related symptoms or organ compromise, such as cytopenias, with no evidence of survival benefit for the treatment of asymptomatic patients. Patients with CLL are not cured with conventional therapy and typically require repeated treatments over their lifetime. Median overall survival (OS) is difficult to estimate given recent advances in therapy options but is dependent upon disease features, patient characteristics, and treatment choice. Since the recent introduction of targeted therapy for CLL, there has been limited Canadian data on the evolution of management approaches in a real-world setting. Data are needed to improve the current understanding of the treatment landscape of CLL. This retrospective observational cohort study used real-world, population-level data to describe the baseline characteristics, treatment patterns, clinical outcomes, and healthcare resource use of individuals diagnosed with CLL in Alberta, Canada. The study cohort included all individuals in Alberta over 18 diagnosed with CLL between 2010-2020 and who subsequently initiated systemic therapy. Data were collected through electronic health records and administrative databases. OS was defined as the date of diagnosis to death from any cause or last known contact with the health care system. A total of 890 individuals diagnosed with CLL between 2010-2020 who initiated first-line (1L) systemic therapy were included in the analyses. The mean age at initiation of 1L therapy was 69 years, and 68% were male. Among individuals who initiated 1L therapy in 2020+, the primary form of 1L systemic therapy was ibrutinib monotherapy (IBR) (29%), followed by bendamustine plus rituximab (BR) (21%). A considerable number of individuals did not initiate subsequent lines of therapy, with a drop of approximately 40-45% between each successive line. The treatment landscape changed over time, particularly with respect to decreased use of 1L fludarabine, cyclophosphamide, and rituximab (FCR; 40% in 2010-2013 vs. 9% in 2020+) and increased use of 1L IBR (6% in 2014-2017 vs. 29% in 2020+). Individuals treated at an academic centre were more likely to receive 1L IBR than those treated at a community centre. The median duration of 1L therapy was considerably longer for patients treated with targeted therapy (IBR: 9.8 months) compared to chemoimmunotherapy (BR: 5.6 months, FCR: 5.6 months). The majority of individuals did not initiate therapy immediately after diagnosis, with a median time from diagnosis to initiation of 1L therapy of 24 months (recognizing that the population described includes only those who initiated 1L therapy such that time to first therapy would be much longer for the entire CLL population). The time to next line of therapy (from the start of each line to the start of the subsequent line) was 17 months from 1L to second-line (2L), 14 months from 2L to third-line (3L), and 12 months from 3L to fourth-line. Median OS from initiation of 1L was 104 months, 2L was 70 months, and 3L was 64 months. On average, individuals had 39 healthcare encounters within the first year of initiating 1L systemic therapy, with an average of one hospitalization, six ambulatory care encounters, 25 non-cancer practitioner encounters, and seven cancer physician visits. The mean number of healthcare encounters within the first year of front-line therapy tended to decrease over time, which may be attributable to the changing treatment landscape. These findings highlight the rapid changes in CLL therapy that have occurred over the last decade. Our results demonstrate the significantly smaller cohorts of real-world patients receiving 2L or later therapies compared to 1L and highlight declining survival with subsequent lines of therapy, and a decreasing trend of healthcare resource use over time.
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 | 0,006 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| 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 ».