EGFR testing practices, treatment choice and clinical outcomes in advanced NSCLC in a real-world setting: A retrospective analysis of a US-based electronic health records database
Bibliographic record
Abstract
OBJECTIVES: Guidelines recommend epidermal growth factor receptor (EGFR) mutation testing for patients with advanced non-small cell lung cancer (NSCLC) and initiation of first-line EGFR tyrosine kinase inhibitors (EGFR-TKIs) for EGFR mutation-positive (EGFRm) NSCLC. We analyzed a nationwide electronic health record-derived de-identified database to describe EGFR testing practices, treatment choice, and outcomes in patients from the United States (US) with advanced NSCLC. MATERIALS AND METHODS: Adults diagnosed with stage IIIB-IV NSCLC January 2015-January 2020, who received first-line treatment from a network of ∼280 US cancer clinics were included. Demographics/characteristics, EGFR status, time from advanced diagnosis to EGFR test result, first-line treatment, time from treatment initiation to discontinuation/death (TTD), next treatment/death (TTNTD), and overall survival (OS) were extracted. RESULTS: 12,577/16,309 (77 %) eligible patients had an EGFR test recorded; 1,914/12,577 (15 %) patients had EGFRm NSCLC. Of 1,778 patients with confirmed EGFRm NSCLC before first-line treatment, 75 % received first-line EGFR-TKIs, 11 % chemotherapy, 9 % immunotherapy, and 4 % other treatment. Of 136 patients with an EGFRm result after initiating first-line treatment, 13 % received EGFR-TKIs, 50 % chemotherapy, 19 % immunotherapy, and 18 % other treatment in first-line. Among patients with EGFRm NSCLC, median time from advanced diagnosis to EGFR test result was shorter in patients who received first-line EGFR-TKIs versus first-line chemotherapy/immunotherapy/other treatment. Patients treated with first-line EGFR-TKIs had significantly improved TTD/TTNTD versus those who received first-line chemotherapy/immunotherapy/other treatment (p < 0.001). OS was significantly longer in patients receiving treatment ≥21 versus <21 days after index (p < 0.001). CONCLUSIONS: Nearly one-quarter of patients with advanced NSCLC in a US health network were not tested for EGFR mutations. Of patients who received a EGFRm result after initiating first-line treatment, 13 % received first-line EGFR-TKIs. These real-world data support the need to improve EGFR testing implementation and time to result to optimize first-line treatment for advanced NSCLC.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| 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".