Immune Checkpoint Inhibitors-Associated Thrombosis: Incidence, Risk Factors and Management
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) target programmed cell death (PD) 1 receptor and its ligand PD-L1, and have become an integral part of treatment regimens in many cancers including lung cancer, renal cell carcinoma, melanoma, and more. Cancer is associated with a significantly increased risk of venous thromboembolism compared to non-cancer patients, and the risks increase further with anticancer therapies including ICIs. Cancer-associated thrombosis can lead to hospitalizations, delayed cancer treatment, and mortality. While thrombosis was not reported as a major complication in initial clinical trials leading to the approval of ICIs, emerging evidence from post-marketing studies revealed concerning risks of thrombosis in patients receiving ICIs. However, results remained heterogenous given differences in study designs and populations. Recent studies also showed that C-reactive protein dynamics might be an easily accessible biomarker for thrombosis and disease response in this population. In addition, early findings indicated that a commonly used anticoagulant for cancer-associated thrombosis, factor Xa inhibitors, might have potential synergistic antitumor effects when combined with ICIs. Herein we will review the current literature on the incidence, risk factors, and management of thrombosis in patients with cancer receiving ICIs. We aim to provide valuable information for clinicians in managing these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".