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Overall survival of patients with chronic obstructive pulmonary disease receiving immunotherapy for non-small cell lung cancer: A population-based analysis in Ontario, Canada.

2022· article· en· W4281707764 on OpenAlexaffabout
Sze Wah Samuel Chan, John R. Goffin, Gregory R. Pond

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsJuravinski Cancer CentreOntario Clinical Oncology GroupMcMaster University
Fundersnot available
KeywordsMedicineCOPDInternal medicineLung cancerNivolumabProportional hazards modelPopulationPembrolizumabOncologyComorbidityCancerImmunotherapy

Abstract

fetched live from OpenAlex

e21172 Background: Outside of clinical trial eligibility criteria, there is limited data to guide the selection of patients with non-small cell lung cancer (NSCLC) for immune checkpoint inhibitor (ICI) therapy. Chronic obstructive pulmonary disease (COPD) and lung cancer are associated, independent of smoking history, with a common background of chronic inflammation. Previous studies have demonstrated that COPD is a negative prognostic marker for NSCLC, but the clinical benefit of ICI in patients with NSCLC and COPD is unknown. Methods: A population-level administrative data analysis of Ontario patients was performed through the Institute of Clinical Evaluative Sciences (ICES) Data Analytic Services. All patients with NSCLC diagnosed between Jan 2010 and Dec 2020 and treated with immune-checkpoint inhibitors (pembrolizumab, nivolumab, atezolizumab) were included. Demographics, comorbidity and marginalization scores, and COPD status were extracted along with outcome information. Overall survival (OS) was estimated using the Kaplan-Meier method, and compared between patients with or without COPD using Cox proportional hazards regression. The frequency of patients requiring hospitalization and duration of treatment was also estimated and compared using the chi-square and Wilcoxon rank-sum test. Results: 73331 NSCLC patients were identified, of which 4.5% (n = 3285) patients received ICI. COPD patients were less likely to receive immunotherapy (3.8% vs. 5.1%, p < 0.001). Among those receiving an ICI, 41% (n = 1362) of patients had a diagnosis of COPD prior to NSCLC diagnosis. Median (95% CI) OS was 17.3 (16.6 to 18.2) months for patients with COPD and 16.9 (16.2 to 17.8) for patients with no known COPD, which was not significantly different in univariate (hazard ratio = 0.96, 95% CI = 0.89 to 1.04, p = 0.35) or multivariate analysis (HR = 0.96, 95% CI = 0.89 to 1.05, p = 0.40). The 5-year survival was also similar between both groups (6.7% vs. 6.5%). The rate of hospitalization within 6 months (18.4% vs 18.0%, p = 0.82) and the duration of immunotherapy treatment (median = 80 vs 71 days p = 0.23) did not differ for the COPD vs. non-COPD groups. Conclusions: Despite an expectation of frailty, our data suggest that NSCLC patients with COPD receiving ICI maintained similar durations of treatment and similar rates of hospitalization, with no significant difference in survival time, compared with those without COPD. While a treatment selection bias cannot be excluded in this non-randomized dataset, our data suggest that a diagnosis of COPD itself should not be considered a contraindication to immune checkpoint inhibitor use in NSCLC.

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)0.001
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 score0.019
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.345
Teacher spread0.323 · 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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Citations0
Published2022
Admission routes2
Has abstractyes

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