Durvalumab-Associated Pneumonitis in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Real-World Population Study
Bibliographic record
Abstract
The PACIFIC trial led to a new standard of care for patients with locally advanced lung cancer, but real-world practice has demonstrated that immune checkpoint inhibitor (ICI) pneumonitis can lead to significant clinical complications. This study aimed to examine the clinical predictors, outcomes, and healthcare utilization data in patients who received consolidation durvalumab. Using the Alberta Immunotherapy Database, NSCLC patients who received durvalumab in Alberta, Canada, from January 2018 to December 2021 were retrospectively evaluated. We examined incidence and predictive values of severe pneumonitis, with overall survival (OS) and time-to-treatment failure (TTF) using exploratory multivariate analyses. Of 189 patients, 91% were ECOG 0–1 and 85% had a partial response from chemoradiation prior to durvalumab. Median TTF and OS were not reached; 1-year OS was 82%. An amount of 26% developed any grade of pneumonitis; 9% had ≥grade 3 pneumonitis. Male gender and a pre-existing autoimmune condition were associated with severe pneumonitis. V20 was associated with any grade of pneumonitis. Pneumonitis development was found to be an independent risk factor for worse OS (p = 0.038) and TTF (p = 0.007). Our results suggest clinical and dosimetric predictive factors of durvalumab-associated pneumonitis. These results affirm the importance of careful patient selection for safe completion of consolidation durvalumab in real-world LA-NSCLC population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".