Early mortality in patients with cancer treated with immune checkpoint inhibitors in routine practice
Bibliographic record
Abstract
BACKGROUND: We sought to estimate the proportion of patients with cancer treated with immune checkpoint inhibitors (ICI) who die soon after starting ICI in the real world and examine factors associated with early mortality (EM). METHODS: We conducted a retrospective cohort study using linked health administrative data from Ontario, Canada. EM was defined as death from any cause within 60 days of ICI initiation. Patients with melanoma, lung, bladder, head and neck, or kidney cancer treated with ICI between 2012 and 2020 were included. RESULTS: A total of 7126 patients treated with ICI were evaluated. Fifteen percent (1075 of 7126) died within 60 days of initiating ICI. The highest mortality was observed in patients with bladder and head and neck tumors (approximately 21% each). In multivariable analysis, previous hospital admission or emergency department visit, prior chemotherapy or radiation therapy, stage 4 disease at diagnosis, lower hemoglobin, higher white blood cell count, and higher symptom burden were associated with higher risk of EM. Conversely, patients with lung and kidney cancer (compared with melanoma), lower neutrophil to lymphocytes ratio, and with higher body mass index were less likely to die within 60 days post ICI initiation. In a sensitivity analysis, 30-day and 90-day mortality were 7% (519 of 7126) and 22% (1582 of 7126), respectively, with comparable clinical factors associated with EM identified. CONCLUSIONS: EM is common among patients treated with ICI in the real-world setting and is associated with several patient and tumor characteristics. Development of a validated tool to predict EM may facilitate better patient selection for treatment with ICI in routine practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".