Indolent Lymphoma Care Delivery and Outcomes during the COVID-19 Pandemic in Ontario, Canada
Notice bibliographique
Résumé
Background At the outset of the coronavirus infectious disease 2019 (COVID-19) pandemic, hematological malignancy was recognized as a risk factor for severe COVID-19 disease, particularly patients with lymphoma, presenting a unique challenge for optimal management of indolent non-Hodgkin lymphoma (iNHL). To this end, professional societies including ASH favoured less immunosuppressive treatment regimens and omission of rituximab maintenance therapy. However, the implications of these recommendations on the care delivery and outcomes of patients with iNHL are unclear. Accordingly, we examined treatment selection, healthcare utilization, and COVID-19 outcomes of patients with iNHL receiving front line systemic treatment during pre-pandemic vs. pandemic period. Methods We performed a retrospective cohort study using administrative databases in Ontario, Canada, comparing outcomes in patients with iNHL who initiated first-line (1L) treatment with rituximab (R) monotherapy, or with bendamustine (B) or cyclophosphamide, vincristine, prednisone (CVP) from Jan 1, 2015 - Dec 31, 2018 (pre-pandemic cohort) and from Sept 1, 2019 - Aug 1, 2020 (pandemic cohort), with end of follow-up Mar, 31 2022. The primary outcome was comparison of treatment pattern before and during the COVID-19 pandemic which includes the following: 1L regimen received (R-CVP vs. B-R vs. R monotherapy), number of cycles received, dose delays, use of rituximab maintenance, and time-to-completion of full rituximab maintenance course (funded in Ontario every 3 months for 8 doses). Secondary outcomes were death, toxicities, healthcare utilization (emergency department [ED] visit, hospitalization, and ICU admission), and COVID-19 outcomes (SARS-CoV-2 PCR-confirmed infection, ED visit, hospitalization, and death). Adjusted odds ratios (aOR) and hazard ratios (aHR) with 95% confidence intervals (CI) from logistic regression and cause-specific proportional hazards models, respectively, were used to estimate associations between covariates (age, sex, comorbidity burden as measured by Aggregated Disease groups) and outcomes. Results We identified 4,143 patients (1,079 pandemic, 3,064 pre-pandemic), median age 69 (interquartile range 60-76) years, 44% female. In both pre- and pandemic periods, B-R was the most frequent prescribed regimen, with no difference in number of cycles given or in treatment delays (Table). During the pandemic, fewer patients received rituximab maintenance (69.6% vs 75.9%; aOR 0.72, 95% CI 0.60-0.88, p=0.0012). Older age was associated with lower odds of rituximab maintenance initiation (age 75+ vs. age < 60 aOR 0.44, 95% CI 0.35-0.56, p<0.0001). Moreover, patients who initiated maintenance during the pandemic period were less likely to complete the full maintenance course (48.7% vs 56.3%) (aHR 0.81, 95% confidence interval [CI] 0.71-0.92, p=0.0010) (Table) (Figure). Patients treated during the pandemic had less healthcare utilization (ED visit aHR 0.77, 95% CI 0.68, 0.88, p<0.0001; hospitalization aHR 0.81, 95% CI 0.70-0.94, p=0.0067) and treatment-related complications (infection aHR 0.69, 95% CI 0.57-0.82, p<0.0001; febrile neutropenia aHR 0.66, 95% CI 0.47-0.94, p=0.020), with no difference in death (aHR 0.79, 95% CI 0.58-1.08, p=0.14, Table). For COVID-19 outcomes, there were 131 SARS-CoV-2 infections from the beginning of the pandemic until December 31, 2021; with follow-up until March 31, 2022, there were 30 ED visits, 56 hospital admissions, and 23 ICU admissions, and 45 deaths related to COVID-19. Rituximab use (first dose to 1-year post-last dose) was associated with higher risk of COVID-19 infection (aHR 1.56, 95% CI 1.09-2.24, p=0.015) and COVID-19 complications (ED visit aHR 4.28, 95% CI 1.79-10.26, p=0.001; hospitalization/death 1.81, 95% CI 1.11-2.93, p=0.016). Primary vaccination series was associated with lower risk of infection and severe outcomes (infection aHR 0.52, 95% CI 0.28-0.97, p=0.04; hospitalization/death aHR 0.36, 95% CI 0.14-0.94, p=0.036; death aHR 0.26, 95% CI 0.07-0.89, p=0.033). Conclusion During the pandemic, B-R remained the preferred regimen for iNHL treatment, while rituximab maintenance use was less. Despite similar 1L regimen use, healthcare utilization and infectious complications were less in the pandemic cohort. Rituximab use was associated with nearly 2-fold risk of COVID-19 hospitalization/death.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».