Immune-Related Adverse Events Associated with Atezolizumab: Insights from Real-World Pharmacovigilance Data
Bibliographic record
Abstract
The advancement of immuno-oncology has brought about a significant shift in cancer treatment methods, with antibody-based immune checkpoint inhibitors like atezolizumab leading the way in this regard. However, the use of this checkpoint blockade can result in immune-related adverse events due to increased T-cell activity. The full spectrum of these events is not yet completely understood. In this study, the United States FDA Adverse Event Reporting System (FAERS) was utilized to investigate immune-related adverse events linked with the use of atezolizumab. The study identified forty-nine immune-related adverse events that affected multiple organ systems, including cardiovascular, respiratory, hematologic, hepatic, renal, gastrointestinal, neurologic, musculoskeletal, dermatologic, endocrine, and systemic disorders. The strongest signals for relative risk occurred for immune-mediated encephalitis (RR = 93.443), autoimmune myocarditis (RR = 56.641), immune-mediated hepatitis (RR = 49.062), immune-mediated nephritis (RR = 40.947), and autoimmune arthritis (RR = 39.382). Despite the morbidity associated with these adverse events, emerging evidence suggests potential associations with improved survival outcomes. Overall, this report sheds light on the widespread immune-related adverse events that cause significant morbidity and mortality in patients with cancer being treated with atezolizumab and brings attention to them for the clinicians treating 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".