PB1821 REAL‐WORLD HEALTHCARE RESOURCE UTILIZATION (HRU) AND COSTS OF DIFFUSE LARGE B‐CELL LYMPHOMA (DLBCL) PATIENTS INITIATED ON ANTI‐CANCER THERAPIES IN THE UNITED STATES (US)
Notice bibliographique
Résumé
Background: DLBCL, the most common type of non‐Hodgkin lymphoma in the US, is associated with significant HRU and healthcare costs. 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 detailed examination of real‐world HRU and costs in patients with DLBCL. Aims: This study aimed to describe real‐world HRU and costs among patients diagnosed with DLBCL who initiated anti‐cancer therapies using a US claim database. Methods: A retrospective database analysis was conducted using the Optum Clinformatics TM Data Mart database (01/2013–03/2018). Patients with ≥1 inpatient 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 DLBCL confirmation) after October 1 st , 2015 (index date for incident patients) were classified as (1) incident if they had no prior ICD‐9‐CM diagnosis code for unspecified DLBCL or PMBCL, or as (2) prevalent if they had a prior ICD‐9‐CM code for unspecified DLBCL or PMBCL before October 2015 (index date for prevalent patients). Patients ≥18 years of age as of the index date with ≥12 months of continuous enrollment pre‐index date (baseline period) were included. Patients with any ICD‐10‐CM diagnosis for PMBCL or baseline diagnoses of Hodgkin lymphoma, multiple myeloma, or other selected lymphomas were excluded. Patients were observed up to the earliest date of end of data availability or end of continuous enrollment in health plans. All‐cause HRU (including inpatient stays, outpatient [OP] visits, emergency room visits, and other visits) and associated costs, including pharmacy costs, were computed per patient per year (PPPY) and reported for all treated patients and those treated with R‐CHOP (i.e., most used 1L treatment). 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. Mean ± standard deviation [SD] total healthcare costs (medical and pharmacy costs) were $137,156 ± 123,753 and $127,202 ± 98,282 for all treated incident patients and those treated with R‐CHOP, respectively. Corresponding OP costs (including costs of administered therapies) were $88,202 ± 89,417 and $87,616 ± 77,362, respectively, and were the main drivers of total healthcare costs. Although similar trends were observed for prevalent patients, mean total healthcare costs were lower for all treated prevalent patients ($81,669 ± 114,414) relative to all treated incident patients; however follow‐up periods were longer for prevalent patients (∼2.5 years) compared to incident patients (∼11 months). A sensitivity analysis restricting patients’ evaluation periods up to 12 months (mean follow‐up periods of 8 months for incident and 11 months for prevalent patients) yielded more similar results (mean ± SD total healthcare costs for all treated patients: $169,776 ± 113,618 incident; $140,786 ± 86,428 prevalent) and highlighted increased costs incurred within the first year following a DLBCL diagnosis. Associated HRU results are presented in the Table below. Summary/Conclusion: Overall, this study highlighted the considerable economic burden of patients with DLBCL, particularly within the first year following diagnosis. 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 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 | 0,001 |
| 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 ».