Impact of age and frailty on acute care use during immune checkpoint inhibitor treatment
Bibliographic record
Abstract
BACKGROUND: Older and medically frail adults are underrepresented in clinical trials evaluating immune checkpoint inhibitors, leading to a knowledge gap on their risk of acute care use and immune-related adverse events. We performed a population-level study to evaluate the impact of age and frailty among older adults receiving immune checkpoint inhibitors on acute care use and immune-related adverse events-related hospitalizations. METHODS: Patients with cancer aged 65 years or older who initiated therapy with immune checkpoint inhibitors between June 2012 and October 2018 (Ontario, Canada) were identified using systemic therapy administration data. The cohort was linked to other databases to obtain covariates and outcomes. Multivariable Cox proportional models evaluated the impact of age and frailty on acute care use and hospitalization for immune-related adverse events. RESULTS: Among 2737 patients, the median age was 73 years; 26% were prefrail, and 4% were frail; 72% required acute care use, whereas 8% had an immune-related adverse event-related hospitalization. Increasing frailty was associated with increased risk of acute care use (prefrail vs robust, adjusted hazard ratio [HR] = 1.20, 95% confidence interval [CI] = 1.07 to 1.36, P = .003; frail vs robust, adjusted HR = 1.45, 95% CI = 1.12 to 1.86, P = .004), but age was not associated with acute care use. Increasing age was associated with reduced risk of hospitalization for immune-related adverse events (adjusted HR = 0.97 per year, 95% CI = 0.95 to 0.99, P = .01); compared with patients aged 65-69 years, patients 80 years of age or older had reduced risk of hospitalization for immune-related adverse events (adjusted HR = 0.63, 95% CI = 0.39 to 1.01, P = .05). Frailty was not associated with immune-related adverse event-related hospitalizations. Associations remained consistent when evaluating age and frailty in the same models. CONCLUSIONS: Age was associated with reduced risk of hospitalization for immune-related adverse events but not acute care use, whereas increasing frailty was associated acute care use but not immune-related adverse event-related hospitalization. Age and frailty may need to be considered independently when evaluating their impact on toxicity among older adults receiving immune checkpoint inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".