Economic Burden in US Patients with Relapsed or Refractory Classical Hodgkin Lymphoma Treated with Brentuximab Vedotin or Chemotherapy after Failure of Autologous Hematopoietic Cell Transplantation
Notice bibliographique
Résumé
Abstract Introduction: Classical Hodgkin lymphoma (cHL) is a lymph node cancer of germinal center B-cell origin. Although most patients diagnosed with cHL are cured with multiagent chemotherapy (ChT), those with advanced-stage disease are more likely to harbor refractory disease or relapse. Brentuximab vedotin (BV) was approved by the FDA in 2011 for patients who have relapsed following autologous hematopoietic cell transplantation (auto-HCT), and for patients ineligible for auto-HCT who have received 2 prior lines of therapy. Real-world data are needed to understand the direct costs of medical care among patients with relapsed or refractory cHL treated with BV or ChT after auto-HCT failure. Methods: US patients with a primary diagnosis of cHL were identified from the Truven MarketScan® databases (Commercial, Medicare, and Medicaid) from August 1, 2011 to June 30, 2016 and followed until last visit. The BV cohort comprised all patients with observed BV use, while the non-BV cohort comprised patients who received ChT after auto-HCT but never received BV. Index therapy was defined as BV-based therapy for the BV cohort and first ChT after auto-HCT failure for the non-BV cohort, and the date of first claim for index therapy was defined as the index date. The BV cohort were further grouped into subcohorts based on any observed receipt of auto-HCT (BV alone vs BV + auto-HCT). Demographics were summarized and descriptively compared between cohorts. The total all-cause costs (inpatient, outpatient, and pharmacy claims) were assessed every 6 months in a 2-year period and also calculated using a standard cost per-patient-per-month (PPPM) metric over the entire follow-up period. All costs were inflated to 2016 US dollars. Results: 795 HL patients met the study eligibility criteria (mean age: 43.0 years; 58% male); 575 (72%) were in the BV cohort and 220 (28%) in the non-BV cohort. Median follow-up time for the BV and non-BV cohorts was 10.9 and 10.6 months, respectively. Patients in these 2 cohorts were similar in terms of sex and prevalence of multiple cancers (Table), although compared to those in the non-BV cohort, patients on BV were younger on average (mean age: 42.9 vs 45.9 years, p=0.0284). Mean PPPM all-cause medical costs were $51,245 for non-BV and $30,387 for BV. The use of BV was associated with higher mean PPPM all-cause pharmacy costs than non-BV ($17,934 vs $7645), but mean PPPM inpatient and outpatient costs were higher for non-BV than for BV ($35,165 vs $8021 and $8436 vs $4431, respectively). Within the BV cohort, 357 (62%) received BV alone and 218 (38%) received BV + auto-HCT. Similarly, there was no significant difference in sex and prevalence of multiple cancers between the 2 groups, although those treated with BV + auto-HCT were younger (mean age: 36.0 vs 47.1 years, p Mean total direct medical costs were calculated at 6, 12, 18, and 24 months from index date for those with available follow-up over the corresponding time period. Costs were consistently higher for non-BV and BV + auto-HCT relative to BV alone. At 24 months, mean total all-cause costs reached $550,000 for non-BV, $497,000 for BV + auto-HCT, and $379,000 for BV alone. Inpatient, outpatient, and pharmacy represent approximately 69%, 16%, and 15% of total all-cause costs for non-BV; 34%, 16%, and 51% for BV + auto-HCT; and 21%, 14%, and 65% for BV alone. These patterns were relatively consistent across all 4 timepoints considered (Table). Conclusions: While drug acquisition costs result in higher pharmacy costs for patients receiving newer agents, lower medical costs (including costs of inpatient and outpatient care for the management of cHL, cHL-related events, and other clinical conditions) may be associated with the use of newer efficacious therapies with potentially lower toxicity; thus, the use of newer agents may lead to overall cost savings. Study support: Bristol-Myers Squibb. Download : Download high-res image (482KB) Download : Download full-size image Disclosures Chen: Bristol-Myers Squibb: Employment. Johnston: Bristol Myers Squibb: Consultancy. Szabo: Bristol Myers Squibb: Consultancy. Connors: Bayer Healthcare: Research Funding; Cephalon: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; NanoString Technologies, Amgen, Bayer, BMS, Cephalon, Roche, Genentech, Janssen, Lilly, Merck, Seattle Genetics, Takeda,: Research Funding; Amgen: Research Funding; Bristol-Myers Squibb: Research Funding; NanoString Technologies: Research Funding; F Hoffmann-La Roche: Research Funding; Genentech: Research Funding; Lilly: Research Funding; Merck: Research Funding; Janssen: Research Funding. Yasenchak: Seattle Genetics: Consultancy; Bristol-Myers Squibb: Consultancy.
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 | 0,001 |
| 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 ».