Impact of afatinib dose modification on safety and effectiveness in patients with EGFR mutation-positive advanced NSCLC: Results from a global real-world study (RealGiDo)
Bibliographic record
Abstract
OBJECTIVES: In the LUX-Lung clinical trials of afatinib in EGFR mutation-positive NSCLC, tolerability-guided dose adjustment reduced the incidence and severity of adverse events while maintaining efficacy. The RealGiDo study evaluated the impact of afatinib dose adjustment in a real-world setting. MATERIALS AND METHODS: This non-interventional, observational study used medical records of EGFR mutation-positive NSCLC patients treated with first-line afatinib. Primary outcomes were adverse drug reaction (ADR) incidence and severity, time to treatment failure (TTF), and time to progression (TTP), relative to LUX-Lung 3 (LL3). RESULTS: 228 patients were enrolled from 13 countries. Baseline characteristics were in line with LL3 but with more Del19 patients (78.1% vs. 49.0%) and fewer Asian patients (43.9% vs. 72.2%); 11.8% had ECOG performance status 2-3. A total of 71 (31.1%) received a modified starting dose of ≤30 mg. Of patients who started with 40 mg, 67.1% underwent dose reductions, 86.5% of which were in the first 6 months. Dose reductions were mainly due to ADRs and were more common in female, East Asian, and low body-weight patients. There were no new safety signals and fewer ≥grade 3 ADRs (28.4% vs. 48.9%) and serious adverse events (5.2% vs. 14.0%) than in LL3. Median TTF and TTP were 18.7 and 20.8 months, respectively, and were not impacted by reduced starting dose or dose modification. CONCLUSION: Real-world data show that afatinib dose adjustments reduced the frequency and intensity of ADRs without compromising effectiveness, highlighting the benefit of tailoring afatinib dose to optimise treatment outcomes and supporting clinical decision-making. The study is registered at clinicaltrials.gov (NCT02751879).
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".