Real-world characterization of patients with cancer admitted with immune-related adverse events (irAEs).
Bibliographic record
Abstract
82 Background: Immune checkpoint inhibitors (ICI) are improving the care of cancer patients. Despite being better tolerated than chemotherapy, there is a risk of developing irAEs which may require hospitalization. Although ICI and irAEs are well studied in clinical trials, there is a paucity of studies characterizing the care patterns for real-world irAEs hospitalizations. Methods: A single centre retrospective chart review (Princess Margaret Cancer Centre, Toronto, ON) identified patients receiving standard of care ICI (2012-2017) hospitalized for irAEs. For hospitalizations, clinico-pathological, investigation and treatment details were collected. Descriptive statistics helped to characterize hospitalizations. Results: Among 697 patients (266 lung, 381 melanoma and 50 genitourinary (GU)) on ICI, 8% (14 lung, 41 melanoma and 2 GU) had at least 1 irAE (range 1-4) hospitalization for a total of 69 hospitalizations. Average length of stay was 12 days (range 1-105). Among hospitalized patients, median age was 60; 63% were male; 29% received ipilimumab monotherapy, 28% pembrolizumab, 22% nivolumab and 22% received combination ICI. The most common irAEs were colitis (52%), pneumonitis (20%), hepatitis (10%) and CNS disease (demyelination, hypophysis) (9%). Cases were admitted directly from clinic (39%), emergency rooms (29%), urgent care clinic (18%) or transferred from another hospital (13%). Most patients (72%) were admitted to oncology; 28% to general medicine. Endoscopy was performed in 21% of admissions with 60% showing evidence of irAE; biopsies were obtained in 16% of admissions and 73% had evidence of irAE. Subspecialty services were involved in 60% of admissions. Most patients received steroids (94%); 17% received Infliximab. While age did not impact length of stay (p = 0.63), patients admitted to oncology had longer admissions compared to general medicine (14 vs 6 days, p = 0.009). Conclusions: irAEs occur at similar rates in the real-world compared to clinical trials. There is significant heterogeneity in the care patterns for irAEs. Patients admitted to oncology had longer average lengths of stay. Further characterizing irAE can help to develop quality indicators that may improve irAE outcomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".