Immune Checkpoint Inhibitor–Associated Cardiovascular Toxic Effects
Bibliographic record
Abstract
Importance: The introduction of immune checkpoint inhibitor (ICI) therapy has improved cancer outcomes but at the cost of adverse events, mainly related to the immune system. Cardiovascular (CV) toxic effects, and especially myocarditis, are of particular concern and are the subject of this position statement by the International Cardio-Oncology Society with representation of experts from oncology, hematology, and cardiology. Observations: CV toxic effects of ICI therapies include inflammation-associated diseases, such as myocarditis, pericarditis, and vasculitis, as well as the aggravation of chronic inflammatory conditions, such as atherosclerosis with acute ischemic complications (myocardial infarction and stroke). Patients taking ICI therapies can also develop cardiac dysfunction, stress-induced cardiomyopathy (Takotsubo or apical ballooning syndrome), and heart failure without inflammatory cell infiltration of the myocardium. Atrial and ventricular arrhythmias can emerge in the setting of a systemic inflammatory milieu, myocarditis, or ischemia. Of all potential CV adverse effects, myocarditis remains of highest concern, although fatality rates have declined over time with a broadening spectrum of presentations ranging from troponin elevation of uncertain significance to smoldering, nonsevere, and severe or fulminant myocarditis. Conclusions and Relevance: Concerns for myocarditis continue to dominate the spectrum of CV toxic effects in patients receiving ICI therapy. Recommendations for management vary according to severity. Multidisciplinary collaborations remain key for managing acute toxic effects and future cancer treatment decisions, including ICI rechallenge. Ischemic heart disease constitutes the main differential diagnosis in these patients, while pericarditis can be concomitantly present, and atrial and ventricular arrhythmias can also complicate the clinical picture. Several gaps in knowledge are identified and require further research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".