Immune Checkpoint Inhibitors in Real‐World Treatment of Older Adults with Non–Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVE To evaluate the efficacy and toxicity of immune checkpoint inhibitors (ICIs) in older patients with advanced non–small cell lung cancer (NSCLC) seen in routine clinical practice. DESIGN Retrospective study. SETTING Single academic institution and its affiliated centers. PARTICIPANTS Patients 70 years or older with advanced‐stage NSCLC seen between April 1, 2015, and April 1, 2017, and treated with ICIs. MEASUREMENTS Efficacy data included overall survival (OS) and time to treatment failure (TTF), stratified by age, comorbidities (Charlson Comorbidity Index [CCI]), and Eastern Cooperative Oncology Group Performance Status (ECOG PS), and estimated using the Kaplan‐Meier method and log‐rank test. Toxicity data included immune‐related adverse events (irAEs), need for glucocorticoids, and hospitalization. The associations of toxicity with age, CCI, and ECOG PS were evaluated using the exact χ 2 test or Fisher exact test. RESULTS We included 75 patients (median age: 74 y; range, 70‐92 y); 53% had a CCI of 3 or higher; 49% had ECOG PS of 2 or higher. Median OS for the whole cohort was 8.2 months (ECOG PS 0‐1 vs ≥2: 13.7 vs 3.8 mo; p < .01). Median TTF was 4.2 months (ECOG PS 0‐1 vs ≥2: 5.6 vs 2.0 mo; p = .02). Overall, 37% of patients experienced irAE of any grade (a total of 37 events); 8% were grade 3 or higher (no ICI‐related deaths). Of those who discontinued ICIs (N = 64), 15% were due to irAEs. Of those who experienced irAEs, 64% required glucocorticoids. Hospitalizations during ICI treatment occurred in 72%. Toxicity generally did not differ by age, CCI, or ECOG PS. CONCLUSIONS Outcomes in our cohort were driven by ECOG PS rather than chronological age or comorbidities. The relatively high rates of ICI discontinuation, use of glucocorticoids, and hospitalization during ICI treatment in our study highlight the vulnerability of older adults with advanced NSCLC even in the immunotherapy era. J Am Geriatr Soc 67:905–912, 2019.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".