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Enregistrement W4389218920 · doi:10.1182/blood-2023-181296

Indolent Lymphoma Care Delivery and Outcomes during the COVID-19 Pandemic in Ontario, Canada

2023· article· en· W4389218920 sur OpenAlexaffabout
Inna Y. Gong, Anca Prica, Zharmaine Ante, Andrew Calzavara, Monika K. Krzyzanowska, Simron Singh, Adam Suleman, Matthew C. Cheung, Michael Crump

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineRituximabPandemicRegimenCohortInternal medicinePediatricsPrednisoneLymphomaDiseaseCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil0,495

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,005
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,047
Tête enseignante GPT0,319
Écart entre enseignants0,272 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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