Racial, ethnic, and socioeconomic disparities in treatment outcomes in patients (pts) with diffuse large B-cell lymphoma (DLBCL): A U.S. real-world study using a de-identified electronic health record (EHR)-derived database.
Notice bibliographique
Résumé
e18514 Background: DLBCL, an aggressive disease, is the most common subtype of non-Hodgkin lymphoma. Few studies have addressed socioeconomic and racial/ethnic disparities in treatment patterns and health outcomes for pts with DLBCL. We present a retrospective cohort study, leveraging real-world data from a nationwide database, to investigate these disparities. Methods: Pts with DLBCL treated with first-line (1L) therapy within 90 days of diagnosis were selected from the nationwide Flatiron Health EHR-derived de-identified database from January 2011 to May 2020. During the study, the de-identified data originated from approximately 280 US cancer clinics (̃800 sites of care). Pts’ baseline characteristics, treatment patterns, overall survival (OS), time to next therapy or death to any cause (TTNTD) were compared between race groups (non-Hispanic White [W], non-Hispanic African American [AA], Hispanic or Latino [H], non-Hispanic Asian [A]) and socioeconomic groups (Medicaid without Commercial [Medicaid] vs Commercial without Medicaid [Commercial]). Baseline characteristics were compared using Fisher’s exact, chi-squared or t-tests. Time to event endpoints were compared using Cox models adjusting baseline characteristics. Results: In total, 4,648 pts with DLBCL (82% W, 7% AA, 8% H, 3% A) were included. Compared with other race groups, W pts were older (mean age: 67 vs 60, 62, 62 [W vs AA, H, A]), had a higher proportion of pts with Eastern Cooperative Oncology Group score ≥2 (8% vs 5%, 4%, 4%), and fewer pts with Medicaid insurance (1.7% vs 5%, 6%, 3%). Across race groups, 1L treatments received were similar; 82% had R-CHOP. There were no significant differences in OS (P = 0.278; HR [AA, H, A vs W]: 0.87, 0.85, 0.84) and TTNTD (P = 0.158; HR: 0.89, 0.88, 1.19). There were statistically significant differences in time from diagnosis to treatment (P < 0.0001; HR: 0.83, 0.79, 1.12), although the magnitude of the median differences were relatively small (22, 24, 25, 19 days [W, AA, H, A]). In pts aged < 65, commercially insured pts had less advanced disease (Group Stage IV: 28% vs 59%), better OS (HR [95% CI]: 0.50 [0.31–0.81], P = 0.005) and later TTNTD (HR: 0.70 [0.48–1.03], P = 0.067) compared with Medicaid insured pts. In pts aged ≥65, commercially insured pts had similar disease stage, OS (HR: 1.09 [0.65–1.84], P = 0.756) and TTNTD (HR: 0.94 [0.61–1.44], P = 0.763) compared with Medicaid insured pts. Insurance was not a significant factor for time from diagnosis to treatment for pts aged < 65 (HR: 1.05 [0.80–1.37], P = 0.727) and ≥65 (HR: 1.05 [0.78–1.42], P = 0.742). Conclusions: In this analysis of over 4,500 pts with DLBCL treated in the real-world, access to commercial insurance was associated with health outcomes in pts under 65 years of age, possibly due to earlier diagnosis; race was not a significant factor.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».