Critically ill adult patients with acute leukemia: a systematic review and meta-analysis
Bibliographic record
Abstract
To describe the use of life-sustaining therapies and mortality in patients with acute leukemia admitted to the intensive care unit (ICU). The PubMed database was searched from January 1st, 2000 to July 1st, 2023. All studies including adult critically ill patients with acute leukemia were included. Two reviewers independently selected the studies, assessed bias using the Newcastle-Ottawa scale for cohort studies, and performed data extraction from full-text reading. We performed a proportional meta-analysis using a random effects model. The primary outcome was all-cause ICU mortality. Secondary outcomes included reasons for ICU admission, use of organ support therapies (mechanical ventilation, vasopressors and renal replacement therapy), hospital, day-90 and one-year mortality rates. Of the 1,331 studies screened, 136 (24,861 patients) met the inclusion criteria and were included in the meta-analysis. Acute myeloid leukemia affected 16,269 (66%) patients, acute lymphoblastic leukemia affected 835 (3%) patients, and the type of leukemia was not specified in 7,757 (31%) patients. Acute respiratory failure (70%) and acute circulatory failure (25%) were the main reasons for ICU admission. Invasive mechanical ventilation, vasopressors and renal replacement therapy, were needed in 65%, 53%, and 23% of the patients, respectively. ICU mortality was available in 51 studies (6,668 patients, of whom 2,956 died throughout their ICU stay), resulting in a metanalytical proportion of 52% (95% CI [47%; 57%]; I 2 93%). In a meta-regression, variables that influenced ICU mortality included year of publication, and intubation rate. Acute respiratory failure is the main reason for ICU admission in patients with acute leukemia. Mechanical ventilation is the first life-sustaining therapy to be used, and also a strong predictor of mortality. This study’s protocol was preregistered on PROSPERO (CRD42023439630).
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".