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Record W3097146628 · doi:10.1182/blood-2020-139654

Retrospective Review of Treatment Patterns and Outcomes of DLBCL in the Very Elderly: A Single Centre Experience

2020· article· en· W3097146628 on OpenAlexaffabout
Farheen Manji, Carolyn Owen

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineInternational Prognostic IndexChemotherapy regimenInternal medicineDiffuse large B-cell lymphomaComorbidityRegimenRituximabRetrospective cohort studyPerformance statusCancerSingle CenterCHOPChemotherapySurgeryLymphoma

Abstract

fetched live from OpenAlex

Treatment of diffuse large B-cell lymphoma (DLBCL) in patients over the age of 80 years is challenging due to existing comorbidities and frailty in the face of an aggressive disease. Standard chemo-immunotherapy can sometimes be difficult to administer to these patients due to toxicities, requiring dose adjustments and interruptions. There are no definitive guidelines to help direct therapy in these patients and treatment decisions are often subjective. The objective of this study was to retrospectively review treatment patterns and outcomes of DLBCL in patients over the age of 80 years. All patients who were referred to the Tom Baker Cancer Centre in Calgary, Alberta from 2012-2017 were retrospectively reviewed. Data collected included patient characteristics, comorbidities that were used to calculate the Charlson Comorbidity Index (CCI), revised International Prognostic Index (IPI), chemotherapy regimen(s), dose reductions and interruptions, and mortality. The primary outcome was overall survival (OS). A total of 123 patients with DLBCL over the age of 80 were referred to the cancer center at diagnosis. The average age was 84.9 years (IQR 82-87.5). At diagnosis, 62.3% of patients had an ECOG≥2 and 65.8% of patients had an IPI greater than 3. The most common comorbidities described were a history of congestive heart failure (20%), diabetes (16%) and MI (20%). Patients received either curative intent chemotherapy (62.6%) or palliative therapy (37.4%). The curative intent chemotherapy regimens included dose-reduced R-CHOP (31.5%), full dose R-CHOP (11.8%), R-CEOP (14.2%), and other chemotherapy regimens (3.9%). Palliative therapy included PEP-C chemotherapy (4.7%) or treatment with steroids (13.4%) or no treatment at all (20.5%). There were 28 patients (22.7%) who received adjunctive radiotherapy for limited stage or bulky disease. Risk factors at diagnosis for mortality included elevated LDH (OR 3.14, CI 1.42-6.93, p<0.05) and ECOG≥2 (OR 2.15, 0.99-4.67, p<0.05). Every 1 point increase in patients' CCI at diagnosis was associated with an increased risk of death (OR 1.38, 95% CI 1.05-1.82, p=0.02). The cumulative OS at 2 years was 44.7%. Patients who received any amount of curative intent chemotherapy did better than the palliative group (See Figure 1). Within the chemotherapy group, 33.3% of patients stopped their treatment early. These patients had better survival than the palliation group (HR 0.56, 95% CI 0.34-0.94, p=0.04) but overall still had a poor prognosis with a median OS of 10.9 months. Very elderly patients with DLBCL still remain a very challenging population to treat with most patients presenting with high risk disease and poor functional status. Without chemotherapy, the overall prognosis is dismal. Patients who are able to complete treatment with chemo-immunotherapy have the best survival despite the risk of toxicities associated with more intensive treatment but that survival is significantly shortened if they are unable to complete their planned cycles. Disclosures Owen: AbbVie, F. Hoffmann-La Roche, Janssen, Astrazeneca, Merck, Servier, Novartis, Teva: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.273
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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