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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".