A systematic evaluation and meta-analysis of early prediction of post-thrombotic syndrome - Yu, Tong; Song, Jialin; Yu, LingKe; Deng, Wanlin (Frontiers in Cardiovascular Medicine, 2023)
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.