Outcomes of Patients With Interstitial Lung Disease Receiving Programmed Cell Death 1 Inhibitors: A Retrospective Case Series
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs), such as programmed cell death 1 (PD-1) inhibitors, are used to treat multiple cancers. Limited data exist as to the use of ICIs in patients with coexistent interstitial lung disease (ILD). We conducted a retrospective case series to assess clinical and radiologic outcomes of patients with ILD treated with PD-1 inhibitors. METHODS: Eligible patients were 18 years of age or older, treated with pembrolizumab or nivolumab for oncologic indications, and had evidence of ILD on chest computed tomography scan not attributable to radiotherapy before initiation of ICI therapy. Outcomes of interest included mortality, hospitalizations for respiratory-related causes, development of pneumonitis, and radiologic change in ILD over a 1-year follow-up period. RESULTS: We included 41 patients in the analysis. At 1 year, 17 patients (41.5%) were alive, 23 had died (56.1%), and 1 (2.4%) was lost to follow-up. Of 23 deaths, 16 (69.6%) were due to cancer, 4 (17.4%) to causes excluding cancer and ILD, and 3 (13.0%) to hypoxemic respiratory failure from ILD- or ICI-induced pneumonitis. Three patients (7.3%) required hospitalization owing to ILD, including drug-induced pneumonitis, and 3 (7.3%) developed pneumonitis attributable to anti-PD-1 therapy. On follow-up computed tomography scans, 32 patients (78.0%) had stable or improved ILD and 9 (22.0%) had progression. CONCLUSION: Patients with ILD receiving PD-1 inhibitors more frequently died of cancer-related causes than from ILD. Further research is needed to determine the safety of ICIs in patients with ILD and if ILD subtype may help to refine ICI-associated risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".