Mortality-Related Risk Factors in Patients with Hematologic Neoplasm Admitted to the Intensive Care Unit: A Systematic Review
Bibliographic record
Abstract
Background: Hematooncology patients admitted to intensive care units (ICUs) are at high risk for mortality due to the severity of their critical illness. Such complications can develop into complex clinical management, thus signaling an urgent need to identify mortality-related factors to improve interventions and outcomes for these patients. Methods: A systematic review of studies published between 2012 and 2023 in databases such as PubMed, Scopus, and Web of Science was conducted, following the PRISMA guidelines. A meta-analysis was carried out to determine the significance of mortality-related factors. Results: In a review of twenty-four studies, it was found that invasive mechanical ventilation (IMV) was associated with an odds ratio (OR) between 2.70 and 8.26 in 75% of the studies. The use of vasopressor support had an OR of 6.28 in 50% of the studies, while pulmonary involvement by tumor had an OR of 6.73 in 30% of the studies. Sepsis showed an OR of 5.06 in 60% of the studies, and neutropenia upon admission increased mortality in 40% of the studies. Severe respiratory failure (PaO2/FiO2 < 150) had an OR of 7.69 in 55% of the studies. Additionally, ICU readmission and late admission were identified as risk factors for increased mortality. Conclusions: Mortality among hematooncology ICU patients is associated with IMV, vasopressor support, pulmonary involvement, sepsis, neutropenia, severe respiratory failure, ICU readmission, and late admission. Identifying and managing these factors in a timely manner can improve survival and the quality of care.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".