Efficacy and hepatotoxicity of Infliximab in resolving steroid-refractory immune-related adverse events (irAEs) in a real-world setting.
Bibliographic record
Abstract
e15227 Background: Infliximab is used to treat steroid-refractory immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICI). Current dogma holds that infliximab is contraindicated in immune hepatitis; steroid refractory disease should be managed with mycophenolate mofetil (MMF). This is based on anecdotal reports of infliximab-induced hepatotoxicity in patients with rheumatologic diseases who received multiple infusions of the drug. In this series of 56 consecutive cases of infliximab-treated patients, we assessed the efficacy of infliximab in resolving specific steroid-refractory irAEs and evaluated the risk of hepatotoxicity from this drug. Methods: We reviewed consecutive patients treated with infliximab for steroid-refractory irAE at a tertiary cancer center between January 2010 and February 2019. To judge hepatotoxicity, we used the Wilcoxon signed rank test to compare the mean value of ALT, AST and total bilirubin (BT) between 0-4 weeks before and after infliximab therapy. We compared factors associated with infliximab efficacy by logistic regression. A p value < 0.05 was considered statistically significant. Results: Mean age was 61.9 years. The majority had a diagnosis of melanoma (62%); 45% had a combination of two ICI before toxicity onset, of whom 73% received an anti-PD1 and an anti-CTLA-4. Colitis was the most common toxicity (66%), followed by pneumonitis (11%). Infliximab was used to treat immune related hepatitis (ir-hepatitis) in 1 patient (2%). Median number of infliximab doses was 1 (1-3), with resolution of toxicity in 76% of patients. Colitis was likely to resolve with infliximab in comparison to other irAE [OR 0.2(95% CI 0.05-0.73)]. Univariate logistic regression did not demonstrate statistical difference in toxicity outcome when ICI were given either as monotherapy (p = 0.3) or combination (p = 0.97). Mean BT, ALT and AST levels before and after infliximab were not statistically different (see table), with no evidence of infliximab-induced hepatotoxicity. The patient treated for ir-hepatitis had a complete recovery, with no further hepatotoxicity. Conclusions: Infliximab was effective in treating irAE and was not associated with hepatotoxicity in this dose-limited setting. Infliximab may be an alternative to MMF in steroid-refractory ir-hepatitis and should be tested prospectively in a randomized trial. [Table: see text]
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".