Meta-Analysis of Long-Term Risk of Recurrent Venous Thromboembolism after Stopping Anticoagulation in Men and Women with First Unprovoked Venous Thromboembolism
Bibliographic record
Abstract
Abstract Background: The optimal duration of anticoagulation after a first unprovoked venous thromboembolism (VTE) is uncertain. Anticoagulant therapy is highly effective at reducing the risk of recurrent VTE, but this clinical benefit is not maintained once anticoagulation is stopped. Current guidelines suggest considering indefinite anticoagulation in all patients with unprovoked who have a non-high bleeding risk. However, this is a weak recommendation based on limited evidence. Deciding whether patients with a first unprovoked VTE should be considered for indefinite anticoagulant therapy requires estimation of the long-term risk of recurrent VTE after stopping anticoagulation. This risk however, is poorly established, hindering decision making. Methods: We performed a systematic review and meta-analysis of randomized clinical trials and prospective observational studies to determine the rate of recurrent VTE in the first year, in the second year, between years 2 and 5, and years 5 and 10; and the cumulative incidence for recurrent VTE at 2, 5 and 10 years after stopping anticoagulation in men and women with first unprovoked VTE, who had completed at least 3 months of initial treatment. Studies were identified through a comprehensive literature search using MEDLINE, EMBASE and the Cochrane CENTRAL databases. Data clarifications were requested from authors of eligible studies. Rates of recurrent VTE were calculated for each study from the total number of recurrent VTE events divided by the person-years of follow-up, and then pooled using random-effects meta-analysis. Results: Fourteen studies involving 6, 446 patients were included in the analysis. Among men with a first unprovoked VTE, the pooled rate of recurrent VTE per 100 person-years after stopping anticoagulation was 11.2 events (95% CI, 9.0-13.6) in the first year; 7.4 events (95% CI, 5.5-9.5) in the second year; 4.4 events/year (95% CI, 3.2-5.7) between years 2 and 5, and 3.8 events/year (95% CI, 1.6-6.9) between years 5 and 10 [Table 1]. Among women with a first unprovoked VTE, the pooled rate of recurrent VTE per 100 person-years after stopping anticoagulation was 8.6 events (95% CI, 6.5-11.0) within the first year; 5.2 events (95% CI, 3.5-7.2) in the second year; 3.0 events/year (95% CI, 1.6-4.7) between years 2 and 5, and 2.0 events/year (95% CI, 1.3-2.9) between years 5 and 10 [Table 1]. In men and women respectively, the cumulative incidence for recurrent VTE was 17.8% (95% CI, 14.0%-21.9%) and 13.4% (95% CI, 9.8%-17.4%) at 2 years, 28.2% (95% CI, 22.0%-34.4%) and 20.9% (95% CI, 14.0%-28.5%) at 5 years, and 40.8% (95% CI, 28.0%-53.9%) and 28.5% (95% CI, 19.5%-38.3%) at 10 years after stopping anticoagulant therapy [Table 2]. Conclusions: Among patients with a first unprovoked VTE who have completed at least 3 months of initial treatment, men have a higher long-term risk of recurrent VTE after stopping anticoagulation, and may be given greater consideration for indefinite anticoagulant therapy. Our findings affirm the importance of considering patient's sex in deciding the optimal duration of anticoagulation, and as such, emphasize the need for individualized, patient-centered approach for the long-term management of unprovoked VTE. Disclosures Carrier: BMS: Honoraria, Research Funding; Leo Pharma: Research Funding; Pfizer: Honoraria; Bayer: Honoraria. Weitz:Bristol-Myers Squibb: Honoraria; Daiichi-Sankyo: Honoraria; Ionis: Consultancy, Honoraria; Janssen: Honoraria; Servier: Honoraria; Novartis: Honoraria; Bayer: Honoraria; Boehringer Ingelheim: Honoraria, Research Funding. Schulman:Boehringer-Ingelheim: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria; Sanofi: Honoraria; Bayer: Honoraria. Couturaud:Pfizer: Research Funding; Bayer: Honoraria, Other: Travel Support; AstraZeneca: Honoraria; Actelion: Other: Travel Support; Intermune: Other: Travel Support; Leo Pharma: Other: Travel Support; Daiichi Sankyo: Other: Travel Support. Becattini:Bayer HealthCare: Other: Lecture Fees; Boehringer Ingelheim: Other: Lecture Fees; Bristol Meyer Squibb: Other: Lecture Fees. Agnelli:Daiichi Sankyo: Other: Personal Fees; Boehringer Ingelheim: Other: Personal Fees; Bayer Healthcare: Other: Personal Fees; Pfizer: Other: Personal Fees; Bristol-Myers-Squibb: Other: Personal Fees. Brighton:Glaxo Smith Klein: Other: Personal Fees; Novo Nordisk: Other: Personal Fees; Bayer: Other: Personal Fees. Lensing:Bayer: Employment. Prins:Pfizer: Consultancy; Daiichi Sankyo: Consultancy. Hutton:Cornerstone Research Group: Honoraria. Palareti:Roche: Membership on an entity's Board of Directors or advisory committees; Werfen: Speakers Bureau; Alfa-Wassermann: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Prandoni:Pfizer: Consultancy; Daiichi-Sankyo: Consultancy; Sanofi: Consultancy; Bayer: Consultancy. Büller:Pfizer: Consultancy, Research Funding; Daiichi-Sankyo: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Sanofi-Aventis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Boehringer Ingelheim: Consultancy, Research Funding; Thrombogenics: Consultancy, Research Funding; Isis: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding. Rodger:Biomerieux: Research Funding.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.068 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".