The Impact of Chronic Obstructive Pulmonary Disease on Immune Checkpoint Inhibitor Effectiveness in Non-small Cell Lung Cancer: A Population Health Study
Bibliographic record
Abstract
SUMMARY: Chronic obstructive pulmonary disease (COPD) and lung cancer are associated diseases. COPD confers a negative prognosis in NSCLC, but the clinical benefit of immune checkpoint inhibitors (ICI) in this population is unclear. A population-level analysis of patients in Ontario, Canada was performed through the ICES (formerly known as the Institute for Clinical Evaluative Sciences) administrative database. Patients with NSCLC and treated with PD-1/PD-L1 immune checkpoint inhibitors between Jan 2010 and Dec 2020 were included. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using Cox proportional hazards regression. Hospitalizations and duration of treatment were compared secondarily using logistic and linear regression. A total of 4306 patients received ICI and 54% of patients had a diagnosis of COPD. Median (95% CI) OS was 9.2 (8.5-9.9) months for patients with COPD and 8.2 (7.3-8.8) for patients without COPD, which was not significantly different (adjusted hazard ratio (aHR) = 0.94, 95% CI, 0.87-1.01, P = 0.092). Similarly, the median time on treatment was not different (85 vs. 99 days, multivariable P = 0.10). However, the 90-day hospitalization rate was decreased in the COPD population (multivariable odds ratio 0.76, 95% CI 0.62-0.94, P = 0.011). Among patients with NSCLC receiving ICI, our data suggest that a diagnosis of COPD does not result in shortened treatment, poorer survival, or higher rates of hospitalization. COPD itself should not be considered a contraindication to ICI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".