Venous Thromboembolism in Patients with Liver Cirrhosis: Findings from the RIETE (Registro Informatizado de la Enfermedad TromboEmbolica) Registry
Bibliographic record
Abstract
Patients with cirrhosis are not only at an increased risk of bleeding but also at risk of venous thromboembolism (VTE). We sought to determine the clinical characteristics, management, and outcomes after VTE in patients with cirrhosis. We used the data from RIETE (Registro Informatizado de la Enfermedad TromboEmbolica), an international registry of patients with VTE, to compare the outcomes in patients with and without cirrhosis. Main outcomes included all-cause mortality, pulmonary embolism (PE)-related mortality, recurrent VTE, and bleeding. Among 43,611 patients with acute VTE, 187 (0.4%) had cirrhosis. Of these, 184 (98.4%) received anticoagulation for a median of 109 days (interquartile range [IQR]: 43-201 days), most commonly with enoxaparin (median dose: 1.77 [IQR: 1.38-2.00] mg/kg/day). Compared with patients without cirrhosis, those with cirrhosis had a higher rate of all-cause mortality (10.7 vs. 3.4%; odds ratio [OR]: 3.41; 95% confidence interval [CI]: 2.03-5.46) and fatal bleeding (2.1 vs. 0.2%; OR: 13.94; 95% CI: 3.65-37.90) but similar rates of fatal PE (0.5 vs. 0.5%; OR: 1.17; 95% CI: 0.03-6.70). Patients with cirrhosis had a higher rate of all-cause mortality per 100 patient-years of follow-up (58.9 vs. 16.0; hazard ratio [HR]: 3.70; 95% CI: 2.69-4.91). One-year hazard ratio of clinically relevant bleeding (HR: 2.86; 95% CI: 1.91-4.27), fatal bleeding (HR: 8.51; 95% CI: 3.5-20.7), or recurrent VTE (HR: 2.08; 95% CI: 1.00-4.36) was higher in patients with cirrhosis. Cirrhosis is a challenging comorbidity in patients with VTE. Most patients were treated with anticoagulation and had an elevated risk of recurrence, similar risk of fatal PE, and a very high risk of bleeding including fatal bleeds.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".