Association between immune checkpoint inhibitor treatment in individuals with cancer and risk of hospitalisation after SARS-CoV-2 infection: a population-based retrospective cohort study
Bibliographic record
Abstract
Introduction Immune checkpoint inhibitor (ICI)-treated patients exhibit greater serological conversion rates following COVID-19 vaccinations compared with those receiving cytotoxic chemotherapy and healthy subjects, but whether those receiving ICIs, which unlike most cancer therapies restore cellular immunocompetence, may be at higher or lower risk of severe SARS-CoV-2 infection. It is unknown. Research design and methods We conducted a retrospective cohort study of individuals with a prior diagnosis of cancer and SARS-CoV-2 infection from March 2020 to June 2021 in Alberta, Canada. Propensity score matching was used to compare outcomes in ICI-treated and non-ICI-treated individuals. Outcomes included all-cause death, all-cause hospitalisations, and COVID-19 hospitalisations between 2 days prior to and 30 days after the index positive SARS-CoV-2 reverse transcription PCR (RT-PCR) test date. Results There were 8938 individuals with cancer and a positive SARS-CoV-2 RT-PCR test, including 237 being treated with ICI at the time of infection. ICI treatment was associated with higher risk of 30-day all-cause hospitalisations (adjusted OR, aOR 1.58, 95% CI 1.13 to 2.21, p=0.007), but not COVID-19 hospitalisations (aOR 1.41, 95% CI 0.97 to 2.05, p=0.07) and 30-day all-cause mortality (aOR 0.61, 95% CI 0.35 to 1.06, p=0.08). Conclusions Individuals with cancer treated with ICIs are at higher risk of hospitalisation after SARS-CoV-2 infection compared with individuals with cancer not treated with ICI therapy. These results should alert clinicians and public health officers to this particularly vulnerable population in future pandemics, and future research should examine the impact of ICI in patients with endemic viral infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".