Real-world outcomes among patients with epidermal growth factor receptor (EGFR) mutated non-small cell lung cancer treated with EGFR tyrosine kinase inhibitors versus immunotherapy or chemotherapy in first-line setting.
Bibliographic record
Abstract
281 Background: While EGFR tyrosine kinase inhibitors (TKIs) are the NCCN-recommended first-line (1L) therapy for non-small cell lung cancer (NSCLC) patients (pts) with EGFR mutation (EGFRm), many pts initiate immunotherapy (IO) + chemotherapy (chemo) prior to receiving EGFRm test results. This study assessed clinical outcomes associated with initiating EGFR-TKI vs other therapies in stage IV EGFRm NSCLC. Methods: A retrospective study was conducted in adults with stage IV EGFRm NSCLC who initiated 1L EGFR-TKI, IO (+ chemo), or chemo alone from 5/2017-12/2018, using Flatiron Health Electronic Health Record data. Treatment patterns were characterized with respect to timing of EGFRm test results. Kaplan-Meier analysis and log-rank tests were used to evaluate the median duration of therapy (DoT) and time to next therapy (TTNT), as proxies for progression-free survival. Adjusted hazards ratios (HR) and 95% confidence intervals (CI) representing the effect of 1L therapy on the risk of discontinuing treatment or death (DoT) and the risk of initiating second-line therapy or death (TTNT) were reported from multivariable Cox proportional hazards models controlling for differences in demographics, smoking history, histology, cancer stage, ECOG score, NCI index, time from diagnosis to 1L initiation, and year of 1L initiation, across treatment arms. Results: Among 593 study pts, mean age was 67.5 years and 65.4% were female. EGFR-TKI was used as 1L therapy for 77.2% of pts (n=458), IO in 13.3% (n=79) and chemo in 9.4% (n=56). 7.2% of EGFR-TKI pts, 54.4% of IO pts, and 57.1% of chemo pts initiated 1L before receiving EGFRm test results. Compared to pts on IO and chemo, pts on EGFR-TKI had longer median DoT (EGFR-TKI: 8.7 months [mo]; IO: 4.8 mo; chemo: 3.0 mo, p<0.01) and median TTNT (EGFR-TKI: 12.3 mo; IO: 6.5 mo; chemo: 4.0 mo, p<0.01). Adjusted analyses showed that compared to pts on IO or chemo, pts on EGFR-TKI had significantly lower risk of discontinuing therapy or death (DoT) and initiating second-line therapy or death (TTNT) (Table). Conclusions: Substantial numbers of pts initiated IO + chemo in 1L and EGFR-TKI was associated with better clinical outcomes than IO + chemo, suggesting the importance of adhering to NCCN-recommended therapy for stage IV EGFRm NSCLC pts. [Table: see text]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".