Renal Impairment, Recurrent Venous Thromboembolism and Bleeding in Cancer Patients with Acute Venous Thromboembolism—Analysis of the CATCH Study
Bibliographic record
Abstract
Objective This article assesses the impact of renal impairment (RI) on the efficacy and safety of anticoagulation in patients with cancer-associated thrombosis from the Comparison of Acute Treatments in Cancer Hemostasis (CATCH) study (NCT01130025). Materials and Methods Renal function was assessed using the Modification of Diet in Renal Disease equation in patients with cancer-associated thrombosis who received either tinzaparin (175 IU/kg) once daily or warfarin for 6 months, in an open-label, randomized, multi-centre trial with blinded adjudication of outcomes. Associations between baseline RI (glomerular filtration rate [GFR] <60 mL/min/1.73m2) and recurrent symptomatic or incidental venous thromboembolism (VTE), clinically relevant bleeding (CRB), major bleeding and death were assessed using Fisher's exact test. Results Baseline-centralized GFR data were available for 864 patients (96% of study population). RI was found in 131 patients (15%; n = 69 tinzaparin). Recurrent VTE occurred in 14% of patients with and 8% of patients without RI (relative risk [RR] 1.74; 95% confidence interval [CI] 1.06, 2.85), CRB in 19% and 14%, respectively (RR 1.33; 95% CI 0.90, 1.98), major bleeding in 6.1% and 2.0%, respectively (RR 2.98; 95% CI 1.29, 6.90) and mortality rate was 40% and 34%, respectively (RR 1.20; 95% CI 0.94, 1.53). Patients with RI on tinzaparin showed no difference in recurrent VTE, CRB, major bleeding or mortality rates versus those on warfarin. Conclusion RI in patients with cancer-associated thrombosis on anticoagulation was associated with a statistically significant increase in recurrent VTE and major bleeding, but no significant increase in CRB or mortality. No differences were observed between long-term tinzaparin therapy and warfarin.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".