Does Pre-Existing Chronic Obstructive Pulmonary Disease Increase the Risk of Checkpoint Inhibitor Pneumonitis in Advanced/Metastatic Non-Small Cell Lung Cancer Treated with Immune Checkpoint Inhibitors?
Bibliographic record
Abstract
Objective: Immune checkpoint inhibitors (ICIs) are front-line treatment options for NSCLC. ICI therapy is associated with a risk of immune-related adverse events (irAEs). Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening irAE. Previous studies have demonstrated that asthma and interstitial lung disease are associated with an increased risk of CIP. We sought to determine whether chronic obstructive pulmonary disease (COPD) is associated with CIP. Methods: This retrospective study examines a cohort of ICI-treated NSCLC patients either with or without chemotherapy at the Anna and Peter Brojde Lung Cancer Centre, Jewish General Hospital in Montreal, Canada between 2014 and 2023. We explored associations between risk factors and CIP using the Mann–Whitney U test or Fisher’s exact test. Analysis of prognostic factors was performed using a logistic regression model. All statistical analyses were carried out using SPSS software, version 24.0 (SPSS, Chicago, IL, USA). p-values of 0.05 or less were considered significant. Results: Of the 327 selected patients on ICIs, 23 experienced an acute respiratory deterioration that was attributed to CIP, 87/327(26.6%) patients had a pre-existing diagnosis of COPD, and 11/87 (12.6%) COPD patients experienced CIP compared to 13/240 (5.5%) non-COPD patients (p = 0.061). There was no statistical or clinically meaningful correlation between COPD severity and CIP. The only variable significantly associated with CIP was a poor ECOG performance status. Among ECOG 1 patients, 18/91 (19.8%) experienced CIP compared to 5/226 (2.2%) of those with an ECOG of 0. A multivariate assessment involving all 327 patients revealed no significant factors affecting CIP development. Conclusions: Our single-institution study revealed that although there was a trend, the presence of COPD was not statistically associated with an increased risk of CIP. Additionally, neither FEV1 nor DLCO had a meaningful impact on the development of CIP in COPD patients. Given these findings, we emphasize the need for larger prospective studies to confirm these observations before drawing definitive clinical recommendations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".