Navigating Cardiotoxicity in Immune Checkpoint Inhibitors: From Diagnosis to Long-Term Management
Bibliographic record
Abstract
The advent of immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment, significantly improving patient outcomes across multiple malignancies. Nonetheless, these therapies are associated with immune-related adverse effects, including cardiotoxicity, which remains a critical concern. This review provides a comprehensive analysis of ICI-related cardiotoxicity, encompassing its pathophysiological mechanisms, risk factors, diagnostic modalities, and management strategies. The onset of cardiotoxicity varies widely, ranging from acute myocarditis to long-term cardiovascular complications. Early identification through clinical assessment, biomarkers, and advanced imaging techniques is crucial for timely intervention. Management strategies include high-dose corticosteroids, other immunosuppressive agents, and supportive therapies, with a focus on balancing oncologic efficacy and cardiovascular safety. Additionally, rechallenging patients with ICIs following cardiotoxic events remains a complex clinical decision requiring multidisciplinary evaluation. As immunotherapy indications expand to include high-risk populations in a curative setting too, optimizing screening, prevention, and treatment strategies is essential to mitigate cardiovascular risks. A deep understanding of the molecular and clinical aspects of ICI-related cardiotoxicity will enhance patient safety and therapeutic decision-making, underscoring the need for ongoing research in this rapidly evolving field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".