Adverse Effects of Immune Checkpoint Inhibitors (Programmed Death-1 Inhibitors and Cytotoxic T-Lymphocyte-Associated Protein-4 Inhibitors): Results of a Retrospective Study
Bibliographic record
Abstract
In recent years the use of immunomodulating therapy to treat various cancers has been on the rise. Three checkpoint inhibitors have been approved by the Food and Drug Administration (ipilimumab, pembrolizumab and nivolumab). The use of these drugs comes with serious adverse events related to excessive immune activation, collectively known as immune-related adverse events (irAEs). We conducted a system-based review of 139 case reports/case series that have described these adverse events between January 2016 and April 2018, found in the PubMed database. There was a broad spectrum of presentations, doses and checkpoint inhibitors used. The most common check point inhibitor observed in our literature review was nivolumab. The most common adverse effects encountered were colitis (14/139), hepatitis (11/139), adrenocorticotropic hormone insufficiency (12/139), hypothyroidism (7/139), type 1 diabetes (22/139), acute kidney injury (16/139) and myocarditis (10/139). The treatment most commonly consisted of cessation of the immune checkpoint inhibitor, initiation of steroids and supportive therapy. This approach provided a complete resolution in a majority of cases; however, there were many that developed long-term adverse events with deaths reported in a few cases. The endocrine system was the mostly commonly affected with the development of type 1 diabetes mellitus or diabetic ketoacidosis being the most frequently reported adverse events. While immunomodulating therapy is a significant advance in the management of various malignancies, it is capable of serious adverse effects. Because the majority of the cases developed pancreatic dysfunction within five cycles of therapy, in addition to the evaluation of other systems, pancreatic function should be closely monitored to minimize adverse impact on patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".