ArticleJournal of thrombosis and haemostasis : JTH2019
Risk prediction of recurrent venous thrombosis; where are we now and what can we add?
Article in Journal of thrombosis and haemostasis : JTH, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- How to determine the optimal duration of anticoagulation for VTE: an evidence-based decision-analytical approach.Blood advances · 2026Article
- Practical guide to management after an acute pulmonary embolism.BMJ open respiratory research · 2025Review
- Investigation into the Mechanisms of Paeoniae Radix Rubra in the Treatment of Venous Thrombosis Using Network Pharmacology, Bioinformatics, and Molecular Docking Techniques.Current pharmaceutical design · 2025Article
- Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP).PLoS medicine · 2019Article
- Risk prediction of recurrent venous thrombosis; where are we now and what can we add?Journal of thrombosis and haemostasis : JTH · 2019Article
- Predicting the risk of recurrent venous thrombosis: What the future might bring.Journal of thrombosis and haemostasis : JTH · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSeveral models are available to predict recurrent venous thrombosis (VT) in patients with unprovoked first events.
objectivesTo validate these prediction models externally.
methodsWithin the MEGA follow-up study (n = 3750), we externally validated the Vienna and DASH score. These models were validated (a) by using the original study's criteria for patients with unprovoked VT and (b) by using our own criteria for unprovoked VT. In addition, absolute recurrence risks based on individual VT risk factors were calculated.
resultsThe recurrence rate was 5.2 (95% CI, 4.6-5.9) per 100 patient-years in those who had a first unprovoked VT according to our definition. For the Vienna model it was 3.4 per 100 patient-years and for DASH 3.8 per 100 patient-years. The C-statistic was 0.62 for Vienna and 0.65 for DASH. The C-statistic declined to 0.58 for both Vienna and DASH when we used our own definition of "unprovoked VT." Within the provoked group a strong gradient in risk was found dependent on the presence of traditional risk factors or biomarkers in a patient.
conclusionsThe ability to distinguish patients' recurrence risks is lower than proposed in the original prediction model studies and dependent on the definition that is used for an unprovoked first event. Furthermore, our results suggest that a more-refined risk estimation is possible, also in patients with a provoked first event, who are currently all classified as low risk.
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