Outcomes of Critically Ill Patients With Hematologic Malignancies: Prospective Multicenter Data From France and Belgium—A Groupe de Recherche Respiratoire en Réanimation Onco-Hématologique Study
Bibliographic record
Abstract
PURPOSE Patients with hematologic malignancies are increasingly admitted to the intensive care unit (ICU) when life-threatening events occur. We sought to report outcomes and prognostic factors in these patients. PATIENTS AND METHODS Ours was a prospective, multicenter cohort study of critically ill patients with hematologic malignancies. Health-related quality of life (HRQOL) and disease status were collected after 3 to 6 months. Results Of the 1,011 patients, 38.2% had newly diagnosed malignancies, 23.1% were in remission, and 24.9% had received hematopoietic stem-cell transplantations (HSCT, including 145 allogeneic). ICU admission was mostly required for acute respiratory failure (62.5%) and/or shock (42.3%). On day1, 733 patients (72.5%) received life-supporting interventions. Hospital, day-90, and 1-year survival rates were 60.7%, 52.5%, and 43.3%, respectively. By multivariate analysis, cancer remission and time to ICU admission less than 24 hours were associated with better hospital survival. Poor performance status, Charlson comorbidity index, allogeneic HSCT, organ dysfunction score, cardiac arrest, acute respiratory failure, malignant organ infiltration, and invasive aspergillosis were associated with higher hospital mortality. Mechanical ventilation (47.9% of patients), vasoactive drugs (51.2%), and dialysis (25.9%) were associated with mortality rates of 60.5%, 57.5%, and 59.2%, respectively. On day 90, 80% of survivors had no HRQOL alterations (physical and mental health similar to that of the overall cancer population). After 6 months, 80% of survivors had no change in treatment intensity compared with similar patients not admitted to the ICU, and 80% were in remission. CONCLUSION Critically ill patients with hematologic malignancies have good survival, disease control, and post-ICU HRQOL. Earlier ICU admission is associated with better survival.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".