PF321 REAL‐WORLD TREATMENT PATTERNS OF PATIENTS DIAGNOSED WITH DIFFUSE LARGE B‐CELL LYMPHOMA (DLBCL) IN THE UNITED STATES (US)
Notice bibliographique
Résumé
Background: DLBCL represents the most common subtype of non‐Hodgkin lymphoma worldwide, but current data is limited on the treatment patterns of patients in clinical practice. Standard of care frontline therapy consists of RCHOP or equivalent. In October 2015, DLBCL administrative claims were differentiated from primary mediastinal large B‐cell lymphoma (PMBCL) with the advent of ICD‐10‐CM disease‐specific codes, allowing a more focused look at these populations. Aims: This study aims to describe real‐world treatment patterns among patients diagnosed with DLBCL in the US. Methods: A retrospective database analysis was conducted using the Optum Clinformatics DataMartTM database (01/2013–03/2018). Patients with ≥1 hospitalization or ≥2 outpatient encounters with an ICD‐10‐CM diagnosis code for DLBCL (or an antecedent ICD‐10‐CM diagnosis of other lymphoma, which may have been assigned before confirmation of DLBCL) after October 1st, 2015 (index date) were classified as incident if they had no prior ICD‐9‐CM diagnosis code for unspecified DLBCL or PMBCL, or as prevalent if they had a prior ICD‐9‐CM code for unspecified DLBCL or PMBCL before October 2015 (index date). At least 12 months of continuous enrollment pre‐index date (baseline period) and ≥18 years of age as of the index date was required. Patients with any ICD‐10‐CM diagnosis for PMBCL were excluded; along with patients with baseline diagnoses of Hodgkin lymphoma, multiple myeloma, or other selected lymphomas. An adapted algorithm developed from previously published studies was used to identify lines of therapy (LOT). Duration of therapy spanned from LOT initiation up to discontinuation of all agents in the LOT, a switch to another LOT, or the addition of a new agent. Results: Among 4,074 DLBCL patients (3,201 incident, 873 prevalent), median (IQR) age was 73 (65–80) years; 46% were female. Incident and prevalent patients had mean Charlson comorbidity index scores of 2.7 and 2.3, respectively. Analysis of treatment patterns (Table 1), showed that 1,877 incident patients (58.6%) were treated with ≥1 LOT (mean ± standard deviation [SD] duration of therapy [DOT]: 81.1 ± 65.9 days), and 22.6% of patients treated received ≥2 LOT (mean ± SD DOT: 74.2 ± 91.9 days). Mean ± SD time from index date to the first line (1L) initiation was 47.1 ± 62.1 days. Similarly, 74.6% of prevalent patients were treated with ≥1 LOT (mean ± SD DOT: 110.1 ± 125.3 days), while 38.4% of patients treated received ≥2 LOT (mean ± SD DOT: 123.2 ± 206.9 days). Their mean ± SD time from index date to 1L initiation was 73.9 ± 158.8 days. The most frequently used 1L therapies of both incident and prevalent patients were R‐CHOP (65.3% and 66.8%), monotherapy with rituximab (7.2% and 7.1%), bendamustine plus rituximab (4.7% and 5.2%), R‐CVP (rituximab, cyclophosphamide, vincristine, and prednisone; 2.5% and 3.4%), and R‐CEOP (cyclophosphamide, etoposide, vincristine, and prednisone; 1.8% and 2.0%). Summary/Conclusion: This real‐world study of DLBCL patients suggests that a substantial proportion of these patients require treatment beyond 1L, highlighting the unmet need within this population. 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 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 | 0,000 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».