Thrombotic events in patients treated with immune checkpoint inhibitors for non-small cell lung cancer: A retrospective multicentric cohort study.
Bibliographic record
Abstract
e21198 Background: Venous thromboembolism is a frequent complication of non-small cell lung cancer (NSCLC) and is associated with a worse prognosis, a reduced quality of life, and increased healthcare costs. Immune checkpoint inhibitors (ICI) are revolutionizing the management of NSCLC, but little is known about their impact on thrombosis. This study aims to define the incidence and clinical relevance of thrombosis in NSCLC patients receiving these treatments. Methods: A retrospective multicentric cohort study including 593 patients from three centers in Canada and France was performed. The cumulative incidence of venous thrombotic events after ICIs was calculated, and the impact of these events on survival and response to treatment was determined. Finally, univariate log-rank tests were performed to identify thrombosis risk factors. Results: The incidence of venous thrombosis in the cohort was 9.9% for an incidence rate of 76.5 thrombosis per 1000 person-years, with most thromboses occurring rapidly after treatment initiation. Thrombosis was not correlated with overall survival, progression-free survival, or objective response to ICIs (summarized in the table below). Age ˂ 65 years old (HR = 1.66; 95 % CI = 1.00 – 2.82) and a delay of less than 12 months from diagnosis to the first ICI treatment (HR = 1.74; 95 % CI = 1.03 – 2.87) were associated with an increased risk of thrombosis. Tumors with PD-L1 > 1% were associated with more thrombosis in the first year since the beginning of therapy (HR = 3.06; 95 % CI = 1.19 – 4.76, p=0.015). Conclusions: This study suggests that the time distribution and incidence of thrombotic events in NSCLC patients treated with ICI are comparable to what is reported in other cohorts of patients treated with chemotherapy. In our cohort, thrombosis was not a prognostic factor for survival or response to therapy. Patient age < 65 and tumors with PD-L1 > 1% were associated to a higher risk of thrombotic events.[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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".