Risk of Venous Thromboembolism in Hospitalized Patients with Sickle Cell Disease
Bibliographic record
Abstract
Abstract Background: Patients with sickle cell disease (SCD) are at an increased risk of developing venous thromboembolism (VTE). However, the underlying risk of VTE complication during hospitalization is unclear in this patient population. We sought to report the incidence of VTE and its associated risk factors in hospitalized SCD patients. Patients/Methods: A retrospective cohort study of SCD patients requiring hospitalization was undertaken at a tertiary care center. Incidence ratios of VTE per hospitalization for different risk factors (Thromboprophylaxis use, central venous catheter (CVC), past history of VTE, surgery during hospitalization) were assessed. Univariate, age adjusted and multivariate Poisson models were estimated accounting for the repeated hospitalizations per patients. Results: A total of 101 patients with at least one hospitalization were included in the study. The mean of number of admissions per patients was 8.9. Overall, 17 out of 896 (1.9%) admissions were complicated by VTE. The incidence of VTE varied by risk factors, from 0.8% in patient without CVC to 6.7% among patients admitted with previous history of VTE. Age adjusted and multivariate Poisson models for incidence rate ratios of VTE per hospitalization among patients with SCD for different risk factors are depicted in Table 1. Conclusion: The risk of VTE seems low in hospitalized SCD. A prior history of VTE and a hospitalization for surgery might be associated with higher risk of VTE complication. Future studies assessing these risk factors to tailor thromboprophylaxis regimens are needed. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".