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Enregistrement W2919654992 · doi:10.1182/blood-2018-99-117640

Management of Diffuse Large B Cell Lymphoma in the Oldest Old—Insight into the Management Decision Process: A Canadian Perspective

2018· article· en· W2919654992 sur OpenAlexaffabout
Annie Lacerte, Marie-Pier Bleau, Jean‐François Castilloux, Flavia De-angelis, Michel Pavic, Tamàs Fülöp

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensHôpital Charles-Le MoyneHôpital FleurimontUniversité de Sherbrooke
Organismes subventionnairesnon disponible
Mots-clésMedicineRituximabDiffuse large B-cell lymphomaInternal medicinePopulationPerformance statusLymphomaChemotherapy regimenCHOPChemotherapySurgery

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Incidence of diffuse large B cell lymphoma (DLBCL), an aggressive but curable Non-Hodgkin Lymphoma (NHL) is increasing in the oldest old. Treatment of these patients is challenging due to advanced age, concomitant comorbidities present in this population and the paucity of data from this population in clinical trials. Aims: to evaluate which parameters are considered by medical oncologists in the management and systemic treatment of DLBCL in the oldest old patients (³80 years old) in our centers. Method: This retrospective study evaluated the management of 118 patients ³80 years old diagnosed with DLBCL between 2006 and 2016 in two Quebec hospitals. Baseline demographics, systemic chemotherapy regimens, and overall survival were obtained. Documentation of patient characteristics influencing management options offered by medical oncologists was analyzed when available. Results: Median age was 83.5 years, maximum age was 96 years old, with a total of 8 patients aged ³90 years, and included 65 (55.1%) men. Median Charlson score was 1 [0-2], with 28 (23.7%) patients having a Charlson score > 2. Main patient characteristics according to management are reported in table 1. Of all 73 patients who received systemic chemotherapy, 38 (52.1%) had either a complete or partial response. Median overall survival (OS) of patients treated with systemic chemotherapy (either rituximab alone, R-CHOP, R-mini-CHOP or R-CEOP) was 54.8 months [95% IC 15.7-93.9 months] compared to 4.6 months for patients that did not received systemic chemotherapy [95% IC 0-12.6 months], p < 0.005. Median number of chemotherapy cycles was 3 (1-6), with 19 patients whom received 6 to 8 cycles. 33 (28%) of the cohort received radiotherapy, mostly as an adjunct to the systemic treatment: only 5 patients received radiotherapy alone. Median OS for the different chemotherapy regimens are reported in table 2. 54.8% of the treated patients received prophylactic G-CSF. Febrile neutropenia developed in 13 (17.8%) patients, 10 of whom were prophylactically treated with G-CSF. We observed 54 deaths in our cohort. Main cause of death was lymphoma: 12 in the untreated group (N= 44) and 7 in the treated group (N= 73). Other known causes were sepsis, heart failure, and respiratory failure. Comorbidities were mentioned as influencing treatment option in 33 files: 16 did not receive systemic treatment whereas 17 received systemic treatment. Median Charlson score for these patients was 2 [1-3]. 14 patients had documented geriatric syndromes (i.e. dementia, malnutrition, frailty, delirium or functional decline) as the main concern for treatment; 9 of them did not receive systemic chemotherapy. Conclusion: Very elderly DLBCL patients, despite their advanced age, still have a significant survival benefit when treated with systemic chemotherapy. In our study, main factors contributing to overall survival were ECOG status and aaIPI as they are known to be important considerations in the management decision process. Interestingly, comorbidities, as measured by the Charlson score, do not seem as important in the management decision process. Instead, geriatric syndromes, some of them potentially reversible, appear to play an important role. Disclosures Pavic: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0,002
score de la tête « metaresearch » (Gemma)0,004
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 enseignants0,179
Score d'incertitude au seuil0,361

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,007
Tête enseignante GPT0,250
Écart entre enseignants0,243 · 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

Citations0
Publié2018
Routes d'admission2
Résumé présentoui

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