Real-world selection of fixed-duration versus continuous therapies for treatment-naïve chronic lymphocytic leukemia/small lymphocytic lymphoma
Notice bibliographique
Résumé
Abstract Introduction: The therapeutic landscape for treatment-naïve (TN) chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) has evolved substantially with the introduction of novel targeted agents. Continuous treatment with BTK inhibitors (BTKi) and fixed-duration (FD) regimens such as venetoclax-obinutuzumab (Ven-O) or ibrutinib-venetoclax (I+V) are now standard options. In the absence of head-to-head trials, treatment selection between targeted FD and continuous therapies is frequently individualized. However, the factors influencing this decision-making process in real-world clinical practice remain incompletely characterized. Methods: This retrospective, population-based study included consecutive patients aged ≥18 years who initiated first-line treatment for CLL/SLL between January 1 and December 31, 2024 in the province of Alberta, Canada. During this period, Ven-O was universally funded via provincial health insurance for TN patients deemed ineligible for fludarabine, while first-line BTKi therapy was reimbursed for those with high-risk genetic features (e.g. del(17p), TP53 mutation, or unmutated IGHV) or contraindications to intravenous therapy. I+V was available in select cases via special access request or private insurance. Legacy chemoimmunotherapy regimens—including fludarabine, cyclophosphamide, and rituximab (FCR); bendamustine-rituximab (BR); and chlorambucil-obinutuzumab (Chl-O)—remained publicly funded for TN CLL/SLL. The study objectives were to determine the real-world distribution of first-line treatment strategies and to identify clinical or demographic factors associated with treatment selection. Clinical trial therapies were excluded. Statistical comparisons were conducted using Fisher's exact test. Results: This study included 148 patients with TN CLL/SLL with a median age of 71 years (range 43-93) at initiation of first-line therapy. IGHV mutation status was unmutated in 73 (49%), mutated in 38 (26%), indeterminate in 18 (12%), and not performed in 19 (13%) patients. FISH analysis for del(17p) was positive in 19 (13%), negative in 110 (74%), and not performed in 19 (13%) patients. NGS detected a TP53 mutation in 1/7 (14%) evaluable patients. Targeted FD therapies were selected for 75 (51%) patients, including Ven-O (n=73, 49%)and I+V (n=2, 1%). Continuous BTKi therapy was administered to 65 (44%) patients, including zanubrutinib (n=39, 26%), acalabrutinib(n=25, 17%), and ibrutinib (n=1, 1%). Despite the availability of funded targeted therapies, 8 (5%) patients received chemotherapy-based regimens, including BR (n=5, 4%), Chl-O (n=1, 1%), or chlorambucil monotherapy (n=2, 1%), due to patient frailty and/or logistical considerations (n=4), physician preference (n=3), or patient preference (n=1). Among the 138 patients treated with either continuous BTKi or Ven-O, BTKi was more frequently selected than Ven-O for patients with del(17p)/TP53 mutation (84% versus 16%, p=0.0002) and age >75 years (66% versus 34%, p=0.0051). No association was observed between treatment selection and other clinical or demographic factors, such as sex, treatment centre, urban versus rural residence, cardiovascular comorbidities, use of anticoagulants or antiplatelets, prior malignancy, and IGHV mutation status. A discussion of >1 treatment option was documented for 98 (66%) patients. Of the 87 patients with a documented rationale for treatment choice, the most common reasons included patient preference for FD treatment in 30 (34%), logistical considerations (e.g. appointment burden, travel time, convenience) in 27 (31%), toxicity profile in 17 (20%), genetic risk (e.g. del(17p)) in 16 (18%), and medical fitness (e.g. age, frailty, comorbidities) in 13 (15%) patients. Conclusions: This real-world, population-based study reveals that FD and continuous targeted therapies are selected with similar frequency as first-line treatments for CLL/SLL. Treatment selection is primarily influenced by age, presence of del(17p)/TP53 mutations, patient preferences, and logistical considerations, emphasizing the role of individualized care and shared decision-making. These findings illustrate the heterogeneity of real-world practice and the complexity of first-line treatment decisions in the era of targeted therapies for CLL/SLL, and emphasize the need for further research to guide optimal therapy selection including head-to-head comparative trials.
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 | 0,003 | 0,008 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».