Comparison of care patterns for hospitalized immune-related adverse events (irAEs) between melanoma patients on combination immune checkpoint inhibitor (ICI) therapy versus ICI monotherapy.
Bibliographic record
Abstract
85 Background: Prior clinical trials in melanoma have demonstrated higher rates of irAEs from combination ICI therapy compared to monotherapy. However, this has not been well studied in the real-world where patients often have greater co-morbidities and less organ reserve. We aim to compare irAEs hospitalizations for melanoma patients on combination vs monotherapy ICIs. Methods: We performed a single centre retrospective chart review (Princess Margaret Cancer Centre, Toronto, ON) for all melanoma patients receiving ICI as standard of care (2012-2017) admitted with irAEs. Data collected include demographics, investigations, management and outcomes of hospitalizations. Descriptive analyses were performed to characterize hospitalizations and compare between ICI combination vs monotherapy groups. Results: Among 381 melanoma patients identified on standard of care ICI, 41 (11%) were admitted for irAE. Among those admitted, 10% received monotherapy with nivolumab, 22% pembrolizumab, 39% ipilimumab and 29% combination ICI. Admission rates were higher among patients receiving combination ICI compared to monotherapy (20% vs 8% p = 0.003). Prevalence of the most common irAEs were similar between combination and monotherapy groups: colitis (58% vs 59%), pneumonitis (8% vs 14%) and hepatitis (8% vs 10%). Less than half received invasive diagnostic tests (i.e, endoscopy) (42% combination vs 35% monotherapy, p = 0.50) with 3 (60%) and 5 (50%) confirming irAEs, respectively. Rates of infliximab use were similar between the combination and monotherapy group (25% vs 21%, p = 0.70). Average length of stay was shorter for patients on combination ICI compared to monotherapy (5 days vs 15 days, p = 0.08). irAE readmission rates were similar between patients receiving combination ICI compared to monotherapy (20% vs 17%, p = 0.65). Conclusions: Despite higher admission rates among patients receiving combination ICI, there was a trend towards shorter hospitalizations. Other outcomes including diagnoses, investigations and management were not significantly different between patients receiving combination vs ICI monotherapy.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".