ReviewFrontiers in cardiovascular medicine2023
A systematic evaluation and meta-analysis of early prediction of post-thrombotic syndrome.
Review in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Risk prediction models for ovarian hyperstimulation syndrome: a systematic review and meta-analysis.BMC pregnancy and childbirth · 2025Pooled it
- Untangling profiles of postthrombotic syndrome using unsupervised machine learning.Blood advances · 2025Trial
- Global burden of venous thromboembolism: incidence and outcome estimates of deep-vein thrombosis and pulmonary embolism.Nature reviews. Cardiology · 2026Review
- Machine learning-based prediction of post-thrombotic syndrome in unprovoked lower extremity deep vein thrombosis: a two-center study incorporating uric acid and clinical risk factors.European journal of medical research · 2026Article
- Deep vein thrombosis as a public health priority: wHO's agenda for Africa 2030.Frontiers in cardiovascular medicine · 2026Review
- Risk Factors Associated With Postoperative Complications Following Endovenous Laser Ablation for Varicose Veins.International journal of vascular medicine · 2025Article
- Prognosis and Factors 4 to 10 Years After Deep Vein Thrombosis: A Long-Term Follow-up Cohort Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Post-thrombotic syndrome (PTS) is the most common long-term complication in patients with deep venous thrombosis, and the prevention of PTS remains a major challenge in clinical practice. Some studies have explored early predictors and constructed corresponding prediction models, whereas their specific application and predictive value are controversial. Therefore, we conducted this systematic evaluation and meta-analysis to investigate the incidence of PTS and the feasibility of early prediction. Methods: We systematically searched databases of PubMed, Embase, Cochrane and Web of Science up to April 7, 2023. Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included articles, and the OR values of the predictors in multi-factor logistic regression were pooled to assess whether they could be used as effective independent predictors. Results: We systematically included 20 articles involving 8,512 subjects, with a predominant onset of PTS between 6 and 72 months, with a 2-year incidence of 37.5% (95% CI: 27.8-47.7%). The results for the early predictors were as follows: old age OR = 1.840 (95% CI: 1.410-2.402), obesity or overweight OR = 1.721 (95% CI: 1.245-2.378), proximal deep vein thrombosis OR = 2.335 (95% CI: 1.855-2.938), history of venous thromboembolism OR = 3.593 (95% CI: 1.738-7.240), history of smoking OR = 2.051 (95% CI: 1.305-3.224), varicose veins OR = 2.405 (95% CI: 1.344-4.304), and baseline Villalta score OR = 1.095(95% CI: 1.056-1.135). Meanwhile, gender, unprovoked DVT and insufficient anticoagulation were not independent predictors. Seven studies constructed risk prediction models. In the training set, the c-index of the prediction models was 0.77 (95% CI: 0.74-0.80) with a sensitivity of 0.75 (95% CI: 0.68-0.81) and specificity of 0.69 (95% CI: 0.60-0.77). In the validation set, the c-index, sensitivity and specificity of the prediction models were 0.74(95% CI: 0.69-0.79), 0.71(95% CI: 0.64-0.78) and 0.72(95% CI: 0.67-0.76), respectively. Conclusions: With a high incidence after venous thrombosis, PTS is a complication that cannot be ignored in patients with venous thrombosis. Risk prediction scoring based on early model construction is a feasible option, which helps to identify the patient's condition and develop an individualized prevention program to reduce the risk of PTS.
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