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Record W3213111836 · doi:10.1182/blood-2021-149469

Incidence of Mental Health Events and Its Association with Survival Among Patients with Diffuse Large B-Cell Lymphoma: A Population-Based Cohort Study

2021· article· en· W3213111836 on OpenAlexaffabout
Inna Y. Gong, Matthew C. Cheung, Kelvin Chan, Sumedha Arya, Neil Faught, Ning Liu, Oreofe O. Odejide, Gregory A. Abel, Michael J. Raphael, Anca Prica, Christopher M. Booth, Thomas M. Kuczmarski, Lee Mozessohn

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreQueen's UniversityCancer Care OntarioInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineDiffuse large B-cell lymphomaPopulationRituximabMental healthComorbidityInternal medicineProportional hazards modelCumulative incidenceCohortPsychiatryLymphomaEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction While prior studies suggest that mental health complications are underdiagnosed and undertreated in patients with cancer, a paucity of data exists for patients with diffuse large B-cell lymphoma (DLBCL). Indeed, mental illness can impact the success of potentially curative treatment for DLBCL including delays in treatment initiation, poor chemotherapy compliance, and suboptimal rates of completion. Accordingly, we aimed to examine the risk of incident mental health events following DLBCL diagnosis, and the association of mental health conditions with overall survival (OS). Methods We conducted a population-based observational study using linked administrative healthcare databases from Ontario, Canada. All Ontario residents aged ≥18 years with DLBCL treated with rituximab-based chemotherapy for curative intent between January 2005 and December 2017 were identified and followed from the date of first rituximab until March 1, 2020. The primary outcome was any incident mental health event (emergency department visit, hospitalization, or outpatient visit for mood disturbance including depression and anxiety, psychotic disorder, or substance-related disorder). Patients with a DLBCL diagnosis without pre-existing mental health comorbidity in the 2-years prior to start of rituximab were matched to mental health condition- and cancer-free controls in a 1:4 ratio based on birth year and sex. The cumulative incidence function was used to estimate incidence of mental health events while accounting for the competing risk of death, and differences were compared using Gray's K-sample test. A cause-specific Cox regression model was used to estimate mental health events up to two-years following rituximab initiation, while controlling for relevant covariates (sex, age modeled in 10-year interval increments, rural vs. urban residence, income quintile, and quartile of sum of aggregated diagnosis groups (ADGs) as a measure of comorbid disease burden (mental health and cancer diagnoses excluded). The secondary outcome was the association of mental health conditions on OS for all identified DLBCL patients, evaluated using Cox regression (with mental health event as time-varying variable). Results We identified 10,299 patients diagnosed with DLBCL and treated with a rituximab-containing regimen in Ontario, with median age 67 years (IQR 56-76), 45.9% female, median ADG score of 9 (IQR 6-11), and median of 6 cycles of rituximab received (IQR 4-6). For patients with available stage data (49.2% of cohort), 34.6% had stage IV at diagnosis. When compared to birth year- and sex-matched controls (n=29,620), DLBCL cases (n=7,405) had a greater comorbidity burden (p<0.001). During a median follow-up of 5.16 years (SD+4.13), 28.8% of DLBCL patients without pre-existing mental health conditions developed an incident mental health event. With adjustment of potential confounders, patients with DLBCL still had significantly higher risk of an incident mental health event compared to controls (adjusted hazard ratio [aHR] 1.29, 95% confidence interval [CI] 1.21-1.39, p<0.0001) (Figure 1, Table 1). Younger age, female sex and higher comorbidity burden were associated with an increased risk of an incident mental health event (Table 1). In addition, having a mental health condition (either pre-existing or after start of rituximab) was associated with a significantly worse one-year OS (aHR 1.11, 95% CI 1.01-1.22, p<0.0001) and at end of follow-up (aHR 1.24, 95% CI 1.16-1.32, p<0.0001; Table 2). Additional factors independently associated with worse overall OS included older age, increased comorbidity, and male sex (Table 2). Conclusions In this large population-based study, patients with DLBCL were found to have a significantly higher risk of incident mental health events compared to controls. Moreover, the presence of a mental health condition was associated with worse survival outcomes. These data suggest that patients with DLBCL, particularly those with pre-existing mental health condition(s), would benefit from routine mental health assessment and management during follow-up, not only for mental health itself but also potentially to improve survival. Figure 1 Figure 1. Disclosures Prica: Astra-Zeneca: Honoraria; Kite 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.329
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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

Labeled directly by 2 models reading the full record.

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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Citations1
Published2021
Admission routes2
Has abstractyes

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