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Record W3111405919 · doi:10.1182/blood-2020-140585

Frailty Is Associated with Increased One-Year Mortality in Patients with Newly Diagnosed Diffuse Large-B-Cell Lymphoma: A Population-Based Study

2020· article· en· W3111405919 on OpenAlexaffabout
Abi Vijenthira, Lee Mozessohn, Chenthila Nagamuthu, Ning Liu, Danielle Blunt, Shabbir M.H. Alibhai, Anca Prica, Matthew C. Cheung

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity Health NetworkInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineDiffuse large B-cell lymphomaPopulationProportional hazards modelInternal medicineRetrospective cohort studyEmergency departmentLymphoma

Abstract

fetched live from OpenAlex

Introduction: Previous studies have demonstrated that frailty is associated with mortality among patients with non-Hodgkin lymphoma including diffuse large B-cell lymphoma (DLBCL). However, no studies have examined frailty in an unselected population-based sample of patients with DLBCL, nor have data on health care utilization been considered as a potential mediator of this relationship. Objective: To determine whether frailty is associated with one-year survival in an unselected population of patients with DLBCL, and examine whether its impact is mediated by health care utilization during chemotherapy treatment. Methods: A retrospective cohort study was conducted using population-based health care data in Ontario, Canada. Patients >65 years diagnosed with DLBCL or transformed follicular lymphoma between January 2006 and December 2017 and receiving first-line chemo-immunotherapy were included. Frailty was defined by modifying a previously validated score developed for use with population-based data in Ontario, comprising 30 multidimensional variables (McIsaac, Ann Surg. 2019;270(1):102-108). Patients were categorized as "frail" (score >0.21) vs. "non-frail" (score ≤0.21). Covariates included age, number of comorbidities based on the Johns Hopkins Aggregated Diagnosis Groups (ADGs), and health care utilization during chemotherapy (defined as emergency department (ED) visit or inpatient hospitalization not resulting in death during treatment, and analysed as a time-varying covariate). Cox regression was performed to examine the association between frailty and one-year mortality (primary outcome). Secondary outcomes included health care utilization, chemo-immunotherapy exposure, and cause of death. Results: 5,527 patients were included in the study. 5,216 patients (94%) had de novo DLBCL, and 311 (6%) of patients had transformed follicular lymphoma. The median age was 75 years (IQR 70-80), and 48% (N=2672) were female (Table 1). 2,699 (49%) of patients were classified as frail (Table 2). Frail patients tended to be older (median age 76 (IQR 71-81) vs. 74 years (IQR 70-79)). The difference in mortality between frail and non-frail patients was most pronounced in the initial year following start of treatment (Figure 1a). Within 90 days of first-line rituximab, 14% (N=370) of frail vs. 7% (N=185) of non-frail patients had died (p<0.0001). Within one-year of first-line treatment, 32% (N=868) of frail patients had died compared to 20% (N=553) of non-frail patients (unadjusted HR 1.8, 95% CI 1.6-2.0, p<0.0001, Figure 1b). Among frail patients who died within 1 year (N=868), 34% (N=298) had only received 1 cycle of chemotherapy. In multivariable modelling controlling for age, number of ADG comorbidities, and health care utilization during chemotherapy, frailty (binary exposure) remained independently associated with one-year mortality (adjusted HR 1.6, 95% CI 1.5-1.8, p<0.0001). The relationship between frailty and survival remained consistent when measured in quartiles (HR 1.6 (95% CI 1.3-1.9) for Q2, 2.0 (95% CI 1.7-2.4) for Q3, 2.7 (95% CI 2.3-3.2) for Q4, p<0.0001, Figure 1b). Frail patients were significantly more likely to receive only 1 cycle of chemotherapy than non-frail patients (14% vs. 7%, p<0.0001). Frail patients also had higher health care utilization during chemotherapy (mean ED visits 0.75 + 1.47 vs. 0.59 + 1.17, p<0.001; mean hospitalizations 0.9 + 1.12 vs. 0.72 + 1.05, p<0.001). Frail patients were also more likely to die of DLBCL (38.3 vs. 29%, p<0.0001). Conclusion: Frailty is significantly associated with one-year mortality in patients with newly diagnosed DLBCL, even after adjusting for age, comorbidities, and health care utilization. Frailty appears to be associated with poor tolerability of chemotherapy and a higher likelihood for requiring acute hospital-based care, and future analyses will explore whether this is related to patients suffering increased treatment-related toxicity. Future analyses of these data will also address whether frail patients who die within one-year of first-line treatment have different clinical characteristics compared to frail patients who survive beyond one year. Future prospective studies may help clinicians understand whether any frailty-related variables are modifiable and the role of alternative treatment strategies for vulnerable patients. Disclosures Prica: astra zeneca: Honoraria; seattle genetics: Honoraria; Gilead: 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 score0.097
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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".

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Citations2
Published2020
Admission routes2
Has abstractyes

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