Predictors of long-term post-thrombotic syndrome following high proximal deep vein thrombosis: a cross-sectional study

Interventional Treatment for Post thrombotic Chronic Venous Obstruction: Progress and Challenges

Chronic venous obstruction (CVO), including non-thrombotic Iliac Vein Lesions (NIVLs) and post-thrombotic syndrome (PTS), presents a significant burden on patients' quality of life (QoL) and healthcare systems. Venous recanalization and stenting have emerged as promising minimally invasive approaches, yet challenges in patient selection, procedural techniques, and long-term outcomes persist. This review synthesizes current knowledge on the interventional treatment of PTS, focusing on the evolution of endovascular techniques and stenting. Patient selection criteria, procedural details, and the characteristics of dedicated venous stents are discussed. Particular emphasis is given on the role of inflow and other anatomical considerations, along with postoperative management protocols for an optimal long-term outcome.

Non-coding RNAs regulating endothelial progenitor cells for venous thrombosis: promising therapy and innovation

Venous thromboembolism, which includes deep venous thrombosis (DVT) and pulmonary embolism, is the third most common vascular disease in the world and seriously threatens the lives of patients. Currently, the effect of conventional treatments on DVT is limited. Endothelial progenitor cells (EPCs) play an important role in the resolution and recanalization of DVT, but an unfavorable microenvironment reduces EPC function. Non-coding RNAs, especially long non-coding RNAs and microRNAs, play a crucial role in improving the biological function of EPCs. Non-coding RNAs have become clinical biomarkers of diseases and are expected to serve as new targets for disease intervention. A theoretical and experimental basis for the development of new methods for preventing and treating DVT in the clinic will be provided by studies on the role and molecular mechanism of non-coding RNAs regulating EPC function in the occurrence and development of DVT. To summarize, the characteristics of venous thrombosis, the regulatory role of EPCs in venous thrombosis, and the effect of non-coding RNAs regulating EPCs on venous thrombosis are reviewed. This summary serves as a useful reference and theoretical basis for research into the diagnosis, prevention, treatment, and prognosis of venous thrombosis.

Inflammasome Signaling, Thromboinflammation, and Venous Thromboembolism

Venous thromboembolism (VTE) remains a major health burden despite anticoagulation advances, suggesting incomplete management of pathogenic mechanisms. The NLRP3 (NACHT-, LRR- and pyrin domain-containing protein 3) inflammasome, interleukin (IL)-1, and pyroptosis are emerging contributors to the inflammatory pathogenesis of VTE. Inflammasome pathway activation occurs in patients with VTE. In preclinical models, inflammasome signaling blockade reduces venous thrombogenesis and vascular injury, suggesting that this therapeutic approach may potentially maximize anticoagulation benefits, protecting from VTE occurrence, recurrence, and ensuing post-thrombotic syndrome. The nonselective NLRP3 inhibitor colchicine and the anti-IL-1β agent canakinumab reduce atherothrombosis without increasing bleeding. Rosuvastatin reduces primary venous thrombotic events at least in part through lipid-lowering independent mechanisms, paving the way to targeted anti-inflammatory strategies in VTE. This review outlines recent preclinical and clinical evidence supporting a role for inflammasome pathway activation in venous thrombosis, and discusses the, yet unexplored, therapeutic potential of modulating inflammasome signaling to prevent and manage VTE.

A systematic evaluation and meta-analysis of early prediction of post-thrombotic syndrome

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.

Systematic Review and Meta-Analysis of the Pooled Rate of Post-Thrombotic Syndrome After Isolated Distal Deep Venous Thrombosis

OBJECTIVE

To identify the rate of post-thrombotic syndrome (PTS) after isolated distal deep venous thrombosis (IDDVT) by performing a meta-analysis of the rate of PTS across randomised and observational studies.

DATA SOURCES

MEDLINE, Embase, the Cochrane Controlled Trials Register, Clinicaltrials.gov, European Union Clinical Trials, International Standard Randomised Controlled Trial Number, and the Australian and New-Zealand Trials Registries.

REVIEW METHODS

This review followed PRISMA guidelines using a registered protocol (CRD42021282136). Databases were searched up to December 2021 and prospective studies reporting the development of post-thrombotic syndrome were included; these were pooled with the meta-analysis.

RESULTS

The results showed a post-thrombotic rate of 17% (95% CI 11 - 26%) (seven studies, 217 cases, 1 105 participants). Heterogeneity was high (I2 = 89%). On meta-regression, the rate of post-thrombotic syndrome was not correlated with the length of follow up (p = .71). Three studies (302 participants) reported the severity of post-thrombotic syndrome: 78% were mild (Villalta score 5 - 9); 11% were moderate (Villalta score 10 - 14), and 11% were severe (Villalta score ≥ 15).

CONCLUSION

The risk of post-thrombotic syndrome after IDDVT was one in five and the risk of severe clinical manifestations, including ulceration, was one in 50. There was significant clinical, methodological, and statistical heterogeneity between studies and a substantial risk of bias from pooled studies. Randomised trials to support interventions for prevention of post-thrombotic syndrome are urgently needed.

Developing and optimizing a machine learning predictive model for post-thrombotic syndrome in a longitudinal cohort of patients with proximal deep venous thrombosis

BACKGROUND

Post-thrombotic syndrome (PTS) is the most common chronic complication of deep venous thrombosis (DVT). Risk measurement and stratification of PTS are crucial for patients with DVT. This study aimed to develop predictive models of PTS using machine learning for patients with proximal DVT.

METHODS

Herein, hospital inpatients from a DVT registry electronic health record database were randomly divided into a derivation and a validation set, and four predictive models were constructed using logistic regression, simple decision tree, eXtreme Gradient Boosting (XGBoost), and random forest (RF) algorithms. The presence of PTS was defined according to the Villalta scale. The areas under the receiver operating characteristic curves, decision-curve analysis, and calibration curves were applied to evaluate the performance of these models. The Shapley Additive exPlanations analysis was performed to explain the predictive models.

RESULTS

Among the 300 patients, 126 developed a PTS at 6 months after DVT. The RF model exhibited the best performance among the four models, with an area under the receiver operating characteristic curves of 0.891. The RF model demonstrated that Villalta score at admission, age, body mass index, and pain on calf compression were significant predictors for PTS, with accurate prediction at the individual level. The Shapley Additive exPlanations analysis suggested a nonlinear correlation between age and PTS, with two peak ages of onset at 50 and 70 years.

CONCLUSIONS

The current predictive model identified significant predictors and accurately predicted PTS for patients with proximal DVT. Moreover, the model demonstrated a nonlinear correlation between age and PTS, which might be valuable in risk measurement and stratification of PTS in patients with proximal DVT.

Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study

Background

Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months.

Materials and methods

The clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold.

Results

In total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points.

Conclusion

Machine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.

Characteristics of Deep Venous Thrombosis in Isolated Lower Extremity Fractures and Unsolved Problems in Guidelines: A Review of Recent Literature

Deep venous thrombosis (DVT) has been characterized by a disorder of venous return caused by abnormal blood clotting in deep veins. It often occurs in the lower limbs and is a common complication in orthopaedics. Therefore, relevant professional organizations domestic and overseas had formulated and constantly updated relevant guidelines to prevent the occurrence of DVT. According to the management strategy of the guidelines, the incidence of DVT can be significantly reduced. However, due to the variety of fractures types, the guidelines cannot expound precautions and characteristics of DVT for all fracture types at present, and there are other related unresolved problems. For example, there is still a lack of consistent optimal strategies for the management of DVT following isolated lower extremity fractures with a higher incidence. The best anticoagulant strategies for patients with upper limb fractures, pediatric fractures, and those combined with other injuries are rarely described in orthopaedic guidelines, but such fractures are common in clinical orthopaedics. The long‐term complications after DVT, such as post‐thrombotic syndrome, are not well‐understood. In the absence of clear guidance, orthopaedic surgeons often resort to empiric anticoagulation or conservative treatment, so the prevention effects of DVT are inconsistent. The purpose of this review is to summarize the characteristics of DVT events after isolated lower extremity fractures and to discuss the unsolved issues in the guidelines by reviewing the previous literature and tracing the history of DVT discovery, to provide more scientific and comprehensive recommendations for the prediction and prevention of DVT.