Risk factors for acute organ failure in intensive care unit patients who receive respiratory support in the absence of non-respiratory organ failure: an international prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Many supposed low-risk intensive care unit (ICU) admissions develop acute organ failure (AOF). Identifying patients at high risk of developing AOF and targeting them with preventative strategies may be effective. Our study question was: in a population of ICU patients receiving positive pressure respiratory support (invasive or non-invasive) in the absence of non-respiratory AOF, what is the 14-day incidence of, risk factors for and time to acute organ failure? METHODS: In an international prospective cohort study, patients receiving positive pressure respiratory support (invasive or non-invasive) in the absence of non-respiratory AOF were enrolled and followed for 14 days. The primary outcome measure was the incidence of any AOF (defined as SOFA 3 to 4) during follow-up. RESULTS: A total of 123 of 766 screened patients (16.1%) were enrolled. Data are reported for 121 patients. In total, 45 out of 121 patients (37.2%) developed AOF. Mortality rates were higher in those with AOF: 17.8% versus 4.0% OR 5.11, P = 0.019) for ICU mortality; and 28.9% versus 11.8% (OR 2.80, P = 0.019) for hospital mortality. Median ICU length of stay was also longer in those with AOF (11 versus 3.0 days; P < 0.0001). Hypoxemic respiratory failure (P = 0.001) and cardiovascular dysfunction (that is, SOFA 1 to 2; P = 0.03) were associated with AOF. The median time to first AOF was two days. CONCLUSIONS: Patients receiving positive (invasive or non-invasive) pressure respiratory support in the absence of non-respiratory AOF are commonly admitted to ICU; AOF is frequent in these patients. Organ failure developed within a short period after admission. Hypoxemic respiratory failure and cardiovascular dysfunction were strongly associated with AOF.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".