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Record W2989439927 · doi:10.1182/blood-2019-127156

Statin and COX-2 Inhibitor Exposure Is Associated with Improved Survival in 4913 Newly Diagnosed DLBCL Patients: A Large Population Based Study

2019· article· en· W2989439927 on OpenAlexaffabout
Liam Smyth, Danielle Blunt, Evgenia Gatov, Chenthila Nagamuthu, Ruth Croxford, Lee Mozessohn, Matthew C. Cheung

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRituximabOncologyDiffuse large B-cell lymphomaInternal medicinePopulationProportional hazards modelRegimenLymphomaImmunology

Abstract

fetched live from OpenAlex

Background: In the era of chemo-immunotherapy, risk factors associated with survival in patients with diffuse large B-cell lymphoma (DLBCL) are largely limited to biologic characteristics of disease. Laboratory based studies have postulated that statins (through inhibition of the geranylgeranylation pathways) can induce apoptosis in DLBCL cells; pre-clinical data suggests an anti-cancer potential for metformin (through inhibition of cancer cell growth by activation of AMPK and inhibition of mTOR pathways) and cox-2 inhibitors (anti-proliferative effects through blockade of PI3K pathway, inhibition of angiogenesis and proliferation by blocking eicosanoid receptors). To date, these potential in-vitro benefits have not been demonstrated consistently in "real world" studies. Our objective was to assess the impact of medicines with a biologic potential on lymphoma outcome in the era of rituximab. Methods: We performed a retrospective population-based study of adults ≥66 years diagnosed with DLBCL or transformed lymphoma treated in Ontario, Canada. Administrative databases held at ICES were used to assess the impact of select medications on patient outcomes. All patients treated, with curative intent, with a rituximab containing regimen between January 2005 and December 2015 were included. A 1-year lookback of medication exposure prior to commencing rituximab was used. Cox regression analyses were performed to determine the relationship between medication exposure and lymphoma outcomes. Additional analyses were completed to control for known confounders of survival, including the number of comorbid conditions. Results: A total cohort of 4913 patients were treated with a rituximab containing regimen, most frequently R-CHOP, during the study timeframe. Median age was 75 years (IQR 70-80); 51% were male. The median number of cycles of chemotherapy was 6 (IQR 3-6). The median number of comorbidities was 11 (IQR 9-14). Sixty-nine percent had a high comorbidity score (≥10); 26.4% moderate (6-9); and 4.7% low (0-5). Where mortality data was available, 52.1% of the cohort died at a median of 1 year, of whom 67% died due to DLBCL. In the year prior to commencing lymphoma therapy 45.7% received statin therapy; 16.3% metformin; and 25.0% cox-2 inhibitor. In the univariate analysis, statin exposure (HR 0.88; 0.8 - 0.97) was associated with improved survival, but exposure to cox-2 inhibitor (HR 0.82; 0.65 - 1.04) and metformin (HR 1.11; 0.98 - 1.26) had a no impact, during this timeframe. Adjusting for time varying exposure and demographic variables including socio-economic factors and comorbidities, we demonstrated that additional exposure to statin and cox-2 inhibitors in the 365 days prior to commencing lymphoma therapy was associated with a survival advantage, when compared to those who were never exposed. Statin exposure for 30 days (HR 0.97 [0.96-0.98]), 180 days (HR 0.84 [0.80-0.89]) and 365 days (HR 0.71 [0.63-0.79]) and cox-2 inhibitor exposure for 30 days (HR 0.95 [0.95-0.98]), 180 days (HR 0.76 [0.66-0.86]) and 365 days (HR 0.57 [0.43-0.74]) were independently associated with improved survival. In contrast metformin exposure had no impact on survival in this cohort. Patients with moderate (HR 1.69; 1.25 - 2.29) and high comorbidity scores (HR 3.16; 2.36 - 4.21) had significantly higher risk of mortality (p<0.0001). Increasing age (p<0.001; HR 1.05 [1.05-1.06]) and male sex (p=0.003; HR 1.13 [1.04-1.23]) were also associated with increased mortality.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.095
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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