MétaCan
Menu
Retour à la cohorte
Enregistrement W4389234076 · doi:10.1182/blood-2023-174816

Considering Older Patients As Candidates for Auto Stem Cell Transplants: A Comprehensive Study on Toxicities and Survival Analysis

2023· article· en· W4389234076 sur OpenAlexaff
Miri Zektser, Katrina Hueniken, Michael Crump, Anca Prica, John Kuruvilla, Robert Kridel, Rodger E. Tiedemann, Armand Keating, Vishal Kukreti

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensUniversity Health NetworkPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineCommon Terminology Criteria for Adverse EventsAutologous stem-cell transplantationProportional hazards modelHematopoietic stem cell transplantationHazard ratioOncologyTransplantationProgression-free survivalComorbidityAdverse effectSurgeryChemotherapyConfidence interval

Résumé

récupéré en direct d'OpenAlex

Introduction: High-dose chemotherapy and autologous stem cell transplantation (ASCT) are commonly used for relapsed/refractory (R/R) Hodgkin (HL) and non-Hodgkin lymphoma (NHL). However, concerns about treatment-related mortality (TRM) and toxicity limit its use in older patients (pts). Existing evidence suggests higher rates of complications and inferior overall survival (OS) in older pts, but conflicting findings suggest that outcomes may be influenced by comorbidities rather than age ( Lahoud O, Curr Oncol Rep. 2015). Our study comparing ASCT outcomes and toxicities in lymphoma pts in younger and older age groups. Methods: In this retrospective study, we analyzed lymphoma pts who underwent ASCT at the Princess Margaret Cancer Centre from January 2015 to December 2019. After Institutional Review Board approval, clinical data was collected from institutional databases and patient charts. The hematopoietic cell transplantation comorbidity index (HCT-CI) and Charlson Comorbidity Index (CCI) were retrospectively calculated. Response assessment was done by Lugano 2014 classification. Grade 3-5 nonhematologic toxicities were collected from admission to day 100 using Common Terminology Criteria for Adverse Events version 5. Overall survival (OS) and progression-free survival (PFS) were calculated from date of transplant to death or disease progression. OS and PFS assessed via Kaplan-Meier; log rank tests and Cox regression performed. The patients were divided into two age groups: Group A (<65 years) and Group B (≥65 years). Results: There were 334 pts who underwent ASCT. 332 pts were analyzed; 2 pts were lost to follow-up and 16 pts were day 1 transfers, making acute toxicity data unavailable. The median age was 53 (range: 18-71), 17% of pts being aged ≥65. The majority of pts (69%) had NHL (table 1). The Eastern Cooperative Oncology Group (ECOG) performance status was ≤1 for 91%. Group B had a higher proportion of pts with ECOG score =2 (18% vs 7%, p < 0.001 ) and higher CCI (88% vs 27% , p < 0.001). NHL and mantle cell lymphoma (MCL) were more prevalent in Group B (93%, 23% vs 68%, 12% respectively, p< 0.001). Thirteen percent of NHL pts were transformed disease. Both groups had similar percentage of pts with central nervous system (CNS) lymphoma (~ 5%). Conditioning regimens varied based on lymphoma type: most of the HL/aggressive lymphomas: Etoposide+Melphalan (ML), CNS lymphoma: Thiotepa based regimen, and MCL Cytarabine + ML+/- Total Body Irradiation. The majority (83%) were treated for R/R disease, while 17% received it as first-line treatment. Prior to transplant, 76% of pts were in CR. Median follow-up was 38 months. Median OS was not reached for Group A, and 73 months for Group B. Five year OS were 82% and 74% respectively (p = 0.05). Median PFS was 75.2 months for Group A and 43.0 months for Group B, with 5-year PFS of 54% and 47% respectively (p = 0.2). Twenty percent of pts died during follow-up, with only 4% being NRM. From the entire cohort 3% of patients died within 100 days post-transplant, with no difference between groups. Three pts died within 100 days of ASCT with 2 deaths from sepsis (Group A) and 1 death from Respiratory failure (Group B). Common transplant-related toxicities did not differ significantly between groups (figure 1). Group B had significantly higher rates of Renal (7%vs 18%), Respiratory (16% vs 6%), and Metabolism abnormalities (14% vs 5%) (p = 0.021-0.028). Group B needed more blood product transfusions by 100 days post-transplant: Packed Cells (mean) 1.7 vs 1.1 units (p 0.08), Platelets (mean) 3 vs 1.9 units (p 0.002). Group B had a longer average length of stay 18 days vs. 14 days (p < 0.001). About 6% of pts required ICU admission, and 4% were discharged to rehabilitation/long-term care facilities, with no group differences. Conclusions: The study concludes that ASCT can be safely performed in appropriately selected elderly pts. TRM rates were not significantly higher in older compared to younger pts with lymphoma. However, older pts did experience higher rates of specific toxicities including renal failure, pulmonary complications, metabolism abnormalities, longer hospital stays, and increased transfusion requirements. In the multivariate analysis, older pts exhibited shorter OS, influenced by lymphoma type and treatment response. Further research is needed to develop risk stratification and geriatric assessments to improve toxicity prediction in elderly pts undergoing ASCT.

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)0,001
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 enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,027
Tête enseignante GPT0,294
Écart entre enseignants0,267 · 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
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetAcute Lymphoblastic Leukemia researchTravaux en français237 207