Mortality Risk Among Patients With Influenza Illness Admitted to the ICU: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Despite vaccination programs and available treatments, seasonal influenza carries a large mortality burden, especially in intensive care unit (ICU) settings. Understanding the influenza mortality burden in ICU settings can inform treatment planning and resource allocation. Nonetheless, surveillance data on mortality in ICU-admitted patients are scarce and estimates vary greatly. This systematic literature review (SLR) and meta-analysis investigated all-cause mortality risk among ICU-admitted patients with influenza in Europe. METHODS: We included observational studies conducted in Europe that reported mortality among patients ≥ 6 months of age with influenza admitted to the ICU. Studies published between January-2009 and December-2019 were included. Quality was assessed using a modified Newcastle-Ottawa scale. Pooled all-cause mortality risk was calculated as a proportion using a random-effects model with an inverse variance method. A sensitivity analysis was also conducted, including only studies identified as having low risk of bias. RESULTS: = 93%). The sensitivity analysis using only studies identified as having low risk of bias produced a pooled mortality risk of 0.25 (95%CI: 0.21, 0.29). CONCLUSIONS: These results indicate that approximately a quarter of patients with influenza admitted to the ICU die, reinforcing the need for effective vaccination programs and treatment optimization.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".