The impact of chronic obstructive pulmonary disease on the risk of immune-related pneumonitis in lung cancer patients undergoing immunotherapy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lung cancer, a leading cause of cancer mortality, poses significant treatment challenges. The use of immune checkpoint inhibitors (ICIs) has revolutionized therapy, but it is associated with immune-related pneumonitis (IRP). This study systematically reviews and analyzes the impact of Chronic Obstructive Pulmonary Disease (COPD) on the risk of IRP in lung cancer patients undergoing immunotherapy. METHODS: Adhering to PRISMA guidelines and using the PICO framework, a comprehensive search across PubMed, Embase, Web of Science, and the Cochrane Library was conducted. Inclusion criteria encompassed peer-reviewed studies involving lung cancer patients treated with ICIs, comparing those with and without COPD. The primary outcome was the incidence and risk of IRP. The Newcastle-Ottawa Scale evaluated study quality. The effect size was calculated using random or fixed-effects models based on the observed heterogeneity. We assessed the heterogeneity between studies and conducted a sensitivity analysis. RESULTS: The search identified 1026 articles, with six meeting the criteria for inclusion. Studies varied in design and geography, predominantly retrospective cohort studies. Patients with COPD had an increased risk of IRP (OR = 1.54, 95% CI [1.24, 1.92, P < 0.01). Subgroup analysis based on radiation therapy exposure (< 40% and ≥ 40%) also indicated a heightened IRP risk in COPD patients. Sensitivity analysis affirmed the robustness of the results, and publication bias was not significant. CONCLUSIONS: Lung cancer patients with COPD undergoing immunotherapy have a significantly increased risk of developing IRP. This highlights the necessity for vigilant monitoring and individualized treatment strategies to improve the safety and effectiveness of immunotherapy in this group.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".