Longitudinal Health Utilities, Symptoms and Toxicities in Patients with ALK-Rearranged Lung Cancer Treated with Tyrosine Kinase Inhibitors: A Prospective Real-World Assessment
Bibliographic record
Abstract
Background: Tyrosine kinase inhibitors (TKIs) have dramatically improved the survival of patients with ALK-rearranged (ALK+) non-small-cell lung cancer (NSCLC). Clinical trial data can generally compare drugs in a pair-wise fashion. Real-world collection of health utility data, symptoms, and toxicities allows for the direct comparison between multiple TKI therapies in the population with ALK+ NSCLC. Methods: In a prospective cohort study, outpatients with ALK+ recruited between 2014 and 2018, treated with a variety of TKIs, were assessed every 3 months for clinico-demographic, patient-reported symptom and toxicity data and EQ-5D-derived health utility scores (HUS). Results: In 499 longitudinal encounters of 76 patients with ALK+ NSCLC, each TKI had stable longitudinal HUS when disease was controlled, even after months to years: the mean overall HUS for each TKI ranged from 0.805 to 0.858, and longitudinally from 0.774 to 0.912, with higher values associated with second- or third-generation TKIs of alectinib, brigatinib, and lorlatinib. Disease progression was associated with a mean HUS decrease of 0.065 (95% confidence interval: 0.02 to 0.11). Health utility scores were inversely correlated to multiple symptoms or toxicities: rho values ranged from −0.094 to −0.557. Fewer symptoms and toxicities were associated with the second- and third-generation TKIs compared with crizotinib. In multivariable analysis, only stable disease state and baseline Eastern Cooperative Oncology Group performance status were associated with improved HUS. Conclusions: There was no significant decrease in HUS when patients with ALK+ disease were treated longitudinally with each TKI, as long as patients were clinically stable. Alectinib, brigatinib, and lorlatinib had the best toxicity profiles and exhibited high mean HUS longitudinally in the real-world setting.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".