First-line osimertinib for patients with EGFR-mutated advanced non-small cell lung cancer: efficacy and safety during the COVID-19 pandemic
Bibliographic record
Abstract
Background: The FLAURA trial demonstrated improved overall survival (OS) with first-line osimertinib for patients with epidermal growth factor receptor (EGFR)-mutated advanced non-small cell lung cancer (NSCLC). We studied the efficacy and safety of osimertinib in a cohort treated during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Patients diagnosed with EGFR-mutated advanced NSCLC between 11 March 2020 to 31 December 2021 who received first-line osimertinib in British Columbia, Canada were identified retrospectively. Kaplan-Meier curves of OS and progression-free survival (PFS) from the start of osimertinib were plotted. The associations of baseline characteristics with PFS, and development of pneumonitis or dose reductions due to toxicity with OS were evaluated with hazard ratios estimated using univariable and multivariable Cox models. Results: The cohort comprised 231 individuals. 58.7% of patients with de novo advanced NSCLC were initially diagnosed after presentation to the Emergency Room. At osimertinib initiation, 31.6% were aged ≥75 years and 45.5% had an Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2. Median PFS and OS were 18.0 months [95% confidence interval (CI): 16.1–26.2] and 25.4 months (95% CI: 20.3–not reached), respectively. On multivariable analysis, age ≥75 years (vs. <75), ECOG PS 2/3 (vs. 0/1), ECOG PS 4 (vs. 0/1), current smokers (vs. never smokers), programmed death ligand 1 (PD-L1) expression ≥50% (vs. <1%), and L858R mutation (vs. exon 19 deletion) were associated with shorter PFS. Among 110 patients who progressed, 33.6% received subsequent therapy. A proportion of 16.5% of the cohort developed grade ≥3 adverse events. Pneumonitis from osimertinib (3.9% incidence) was weakly associated with shorter OS (hazard ratio: 2.59, 95% CI: 0.94–7.12, P=0.066); dose reductions were not associated with worse OS. 10.8% of patients developed COVID-19. Conclusions: In a cohort receiving first-line osimertinib during the COVID-19 pandemic, ECOG PS ≥2 was observed in nearly half of patients at treatment initiation contributing to a median OS shorter than in FLAURA. The incidence of severe adverse events was low and dose reduction for drug toxicity did not impact OS. Identifying and reducing barriers to the diagnosis of NSCLC during the COVID-19 pandemic are required.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".