P647: RACIAL DISPARITIES IN REAL-WORLD TREATMENT PATTERNS AND OUTCOMES AMONG PATIENTS WITH CLL
Notice bibliographique
Résumé
Background: Treatment outcomes for patients with chronic lymphocytic leukemia (CLL) have improved with the introduction of targeted agents. Prior research has shown that Black patients (pts) with CLL had shorter survival compared to White non-Hispanic pts. Here, we assess for the first time if there are disparities for Black pts in treatment options and outcomes in real-world clinical practice in the contemporary era of targeted agents. Aims: To assess demographic, clinical, and treatment characteristics and outcomes in first-line (1L) therapy for Black and White pts. Methods: From the 19 US-based centers of the CLL Collaborative Study of Real-World Evidence (CORE), a retrospective, international, observational study, adult Black and White non-Hispanic pts with CLL were selected if they initiated 1L therapy on/after 01/01/2014 outside of clinical trials and classified into 2 mutually exclusive cohorts. Cohorts were balanced using stabilized inverse probability treatment weighting (S-IPTW) on sex, age at 1L, year of 1L, time from CLL diagnosis to 1L, insurance type, ECOG, Rai stage, IGHV, del(17p)/TP53 mutations, and number of comorbidities. Balance was assessed using standardized mean difference and variance ratio. Demographic, clinical, and treatment characteristics were assessed at 1L start. 1L treatment outcome was progression-free survival (PFS). Results: Of the 979 pts included, 110 pts (11.2%) were Black, 336 (34.3%) female (Black: 40[36.4%], White: 296[34.1%]), median age at diagnosis was 63.3 years (Black: 63.3, White: 63.3) with a median follow-up of 22.4 months (mos; Black: 20.2, White: 23.2). Before weighting, Black pts were younger at 1L start (median age: 64.4 vs 66.6), had a higher proportion of pts on Medicaid/no insurance (10.9% vs 3.3%), lacked Rx benefits (10% vs 3.8%), and started 1L sooner after CLL diagnosis (2.8 vs 20.9 mos) relative to White pts. Black pts also had a higher proportion with unmutated IGHV (75.8% vs 62.9%), ATM (16.2% vs 9.3%), and NOTCH1 (8.1% vs 4.2%) mutations, higher comorbidity burden (median number of comorbidities: 2 vs 1), and a lower proportion of pts with 17del/TP53 mutations (13.6% vs 17.9%) relative to White pts. Similarly, a higher proportion of Black pts had only 1 line of treatment (65.5% vs 62.1%), never received a targeted agent during follow-up (30.0% vs 23.2%), and were treated with CT/CIT in 1L (48.2% vs 34.4%); between 2014-2016 (63.3% vs 48.9%) relative to 2017-2022 (37.9% vs 23.9%). Median 1L treatment duration was ~14 mos in both cohorts. Median PFS was 14.8 mos shorter for Black than White patients (Figure 1). After weighting, the cohorts were well-balanced in their characteristics, yet the median PFS remained shorter for Black than White pts (10.4 mos shorter); 11.6 mos shorter in 2014-2016 though not in 2017-2022 (1.8 mos longer; Figure 1). Summary/Conclusion: Racial disparities were observed in clinical characteristics, comorbidity burden, and type of therapy at the time of treatment initiation for 1L CLL, resulting in a large disadvantage in the median PFS for Black pts in the years immediately following the advent of targeted agents. However, after weighting, this difference in the median PFS was attenuated relative to White pts. These data suggest that access to adequate care with more effective targeted treatments may help reduce racial disparities for pts in real-world settings, as well as reducing their comorbidity burden. Our study highlights the importance of inclusion of pts from different backgrounds in clinical trials to study clinical outcomes prospectively.Keywords: Chronic lymphocytic leukemia
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».