Association between Arterial Oxygen and Mortality Across Critically Ill Patients with Hematologic Malignancies: Results from an International Collaborative Network
Bibliographic record
Abstract
Abstract Rationale: Patients with hematological malignancies are at high risk for life-threatening complications. To date, little attention has been paid to the impact of hyperoxemia and excess oxygen use on mortality. Objectives To investigate the association between partial pressure of arterial oxygen (PaO2) and 28-day mortality in critically ill patients with hematologic malignancies. Methods Data from three international cohorts (Europe, Canada, Oceania) of patients who received respiratory support (noninvasive ventilation, high-flow nasal cannula, invasive mechanical ventilation) were obtained. We used mixed-effect Cox models to investigate the association between day one PaO2 or excess oxygen use (inspired fraction of oxygen ≥ 0.6 with PaO2 > 100 mmHg) on day-28 mortality. Measurements and Main Results: 11,249 patients were included. On day one, 5716 patients (50.8%) had normoxemia (60 ≤ PaO2 ≤ 100 mmHg), 1454 (12.9%) hypoxemia (PaO2 < 60 mmHg), and 4079 patients (36.3%) hyperoxemia (PaO2 > 100 mmHg). Excess oxygen was used in 2,201 patients (20%). Crude day-28 mortality rate was 40.6%. There was a significant association between PaO2 and day-28 mortality with a U-shaped relationship (p < 0.001). Higher PaO2 levels (> 100 mmHg) were associated with day-28 mortality with a dose-effect relationship. Subgroup analyses showed an association between hyperoxemia and mortality in patients admitted with neurological disorders; however, the opposite relationship was seen across those admitted with sepsis and neutropenia. Excess oxygen use was also associated with subsequent day-28 mortality (aHR[95%CI]: 1.11[1.04–1.19]). This result persisted after propensity score analysis (matched HR associated with excess oxygen:1.31 [1.20–1.1.44]). Conclusion In critically-ill patients with hematological malignancies, exposure to hyperoxemia and excess oxygen use were associated with increased mortality. Heterogeneity of treatment effect was noted. This might be a modifiable factor to improve mortality.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".