A 10-year review of iliofemoral deep vein thrombosis – are they more dangerous than their distal counterparts?
Bibliographic record
Abstract
Iliofemoral deep vein thrombosis (IFDVT) is associated with potential for poor outcomes despite optimal anticoagulation therapy. To characterize the real-world management of IFDVT in an Australian population. Retrospective evaluation of IFDVT cases managed at Northern Health, Australia from January 2011 to December 2020 was performed and compared to non-iliofemoral lower limb DVTs (non-IFDVT) (n = 1793). 375 IFDVT episodes (median age 69 years; 54.7% female (n = 205)) were diagnosed with median follow-up 56 months. 61.6% (n = 231) were provoked events, including 100 episodes (26.7%) of cancer-associated thrombosis. 24.8% (n = 93) of patients had concomitant pulmonary embolism. Eleven cases underwent endovascular intervention including all seven patients with May-Thurner syndrome. Non-cancer patients with IFDVT received longer duration of anticoagulation (8 vs. 6 months, p < 0.001) or indefinite anticoagulation (28.7% vs. 16.0%, p < 0.001) compared to those with non-IFDVTs. Venous thromboembolism (VTE) recurrence (2.3/100PY, HR 0.839, 95% CI 0.562–1.255, p = 0.390) and major bleeding (2.7/100PY, HR 1.679, 95% CI: 0.876–3.220, p = 0.119) were comparable but the 30-day all-cause mortality (5.1% vs. 1.2%, p < 0.001) including thrombosis-related deaths (1.8% vs. 0.4%, p = 0.004) was more common in the non-cancer IFDVTs. In cancer patients, VTE recurrence rate (4.3/100PY, p = 0.421) was similar but major bleeding (12.4/100PY, p = 0.043) and 30-day mortality (23.0%, p = 0.026) was higher compared to IFDVT patients without active cancer. While the VTE recurrence and major bleeding were comparable between patients with IFDVT and non-IFDVTs, 30-day mortality (including thrombosis-related death) was higher in patients with IFDVT, suggesting a higher risk cohort that warrants careful assessment particularly during the acute period post diagnosis. Visual Summary of A 10-year review of iliofemoral deep vein thrombosis – are they more dangerous than their distal counterparts? This graphical abstract illustrates the key findings from a decade-long retrospective study comparing iliofemoral deep vein thrombosis (IFDVT) with non-IFDVT. It summarizes the main comparative outcomes assessed (30-day mortality, VTE recurrence, and major bleeding), and highlights the increased early mortality risk associated with IFDVT, as well as the distinct impact of active malignancy on IFDVT patient outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".