Long QT syndrome after using EGFR-TKIs in older patients with advanced non-small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Long QT syndrome (LQTS) has been reported in older patients with advanced non-small cell lung cancer (NSCLC) following the use of osimertinib, the third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI). However, there have not been analytic epidemiology studies on this topic. We aimed to compare the risk of LQTS between osimertinib and first/second-generation EGFR-TKIs in older patients with advanced NSCLC. RESEARCH DESIGN AND METHODS: This retrospective observational study used the 2006-2019 Surveillance, Epidemiology, and End Results (SEER)-Medicare data and included older patients with advanced NSCLC who were treated with either osimertinib or first/second-generation EGFR-TKIs during 2007-2017. Inverse probability of treatment weighting (IPTW) was used to balance the two groups with propensity scores estimated based on the patients' socioeconomic and clinical characteristics. Crude incidence rate (IR) and adjusted hazard ratio (HR) of the primary outcome, incident LQTS, were estimated. RESULTS: A total of 545 and 1,135 patients were included in the osimertinib and first/second-generation EGFR-TKI groups, which increased to 1,614 and 1,659, respectively, after IPTW. The osimertinib group had a higher IR of LQTS (2.62 per 100 person-years, 95% CI 2.03-3.38) compared to the first/second-generation EGFR-TKI group (1.33 per 100 person-years, 95% CI 0.92-1.92). After adjusting for covariates, the osimertinib group had a higher risk of LQTS than the first/second-generation EGFR-TKI group, with an HR of 1.94 (95% CI 1.23-3.08). The increased LQTS risk in the osimertinib group was even higher in females, whites and patients aged ≥ 75. CONCLUSIONS: Given the elevated risk of LQTS associated with osimertinib user, close monitoring for cardiac rhythm irregularities of high-risk patients following initiation of EGFR-TKI is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".