The evolution of mortality from sepsis in patients with cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Increasing numbers of patients with cancer are being admitted to intensive care units (ICU) with sepsis. The mortality from sepsis and septic shock in these patients is unclear. This study aimed to establish mortality from sepsis and septic shock in patients with cancer admitted to ICU and assess mortality trends over time. Methods: We conducted a literature search using MEDLINE and EMBASE. Included studies enrolled adult patients with cancer admitted to ICU with sepsis or septic shock and reported outcomes of interest. Studies were assessed using the Newcastle-Ottawa Scale for risk of bias and the quality assessment tool for observational cohort and cross-sectional studies. We performed a meta-analysis to estimate pooled ICU, hospital and 30-day mortality from sepsis and septic shock and a multivariate meta-regression to assess mortality trends over time. The study was registered on PROSPERO (CRD42022341277). Results: Twenty-five articles were included. The pooled ICU, hospital and 30-day mortality for sepsis was 44% (95% CI 38%-50%), 54% (95% CI 49%-60%) and 49% (95% CI 44%-55%) respectively. The pooled ICU, hospital and 30-day mortality for septic shock was 51% (95% CI 45%-57%), 62.6% (95% CI 56%-69%) and 54% (95% CI 46%-61%) respectively. There was significant heterogeneity between studies. The meta-regression identified decreasing ICU and hospital mortality from sepsis, and decreasing ICU mortality from septic shock. Conclusion: Patients with cancer admitted to ICU with sepsis face a significant mortality risk greater than that of the general population, despite decreasing mortality over time. Further research is required to improve outcomes.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".