Incidence of venous thromboembolic disease and risk of bleeding in critically ill patients with hematologic malignancies: A retrospective study
Bibliographic record
Abstract
Objective Our objectives were to describe the use of thromboprophylaxis and the incidence of VTE/bleeding in critically ill patients with hematologic malignancies (HM). Design Retrospective cohort study (2014–2022). Setting Medic-Surgical Intensive Care Unit (ICU) in a tertiary care academic center. Patients Adult patients admitted to ICU with a concomitant diagnosis of a hematological malignancy. Interventions None. Main variables of interest We analyzed demographic data, use of thromboprophylaxis and secondary outcomes that included incidence of VTE (venous thromboembolism), bleeding , mortality, severity scores and organ support. We applied a multivariable logistic regression model to examine the risk of thrombosis in the ICU. Results We included 862 ICU admissions (813 unique patients). Thromboprophylaxis was given during 65% of admissions (LMWH 14%, UFH 8%, and SCDs 43%); in 21% it was contraindicated due to thrombocytopenia ; 14% of cases lacked documentation on prophylaxis. There were 38 unique incident cases of VTE (27 DVT , 11 PE), constituting 4.4% of ICU episodes. Most of VTE cases happened in patients with various degrees of thrombocytopenia. In the multivariable analysis, SOFA score on the first ICU day was independently associated (OR 0.85, 95% CI 0.76−0.96) with the risk of VTE. Bleeding occurred in 7.2% (minor) and 14.4% (major) of episodes; most frequent sites being CNS , abdomen/GI and pulmonary. Conclusions In this cohort of critically ill patients with HM, there was considerable variability in the utilization of DVT prophylaxis, with predominant use of SCDs. The incidence of VTE was 4.4% and major bleeding 14%. Clinical Trial Registration NCT05396157. Venous Thromboembolism in Hematologic Malignancy and Hematopoietic Cell Transplant Patients: a Retrospective Study ( https://clinicaltrials.gov/ ).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".