External validation of the SOX-PTS score in a prospective multicenter trial of patients with proximal deep vein thrombosis

Acute phase determinant of post-thrombotic syndrome: A review of the literature

BACKGROUND

Post-thrombotic syndrome (PTS) is the main long-term complication of deep vein thrombosis (DVT). Several therapies are being evaluated to prevent or to treat PTS. Identifying the patients most likely to benefit from these therapies presents a significant challenge.

OBJECTIVES

The objective of this review was to identify risk factors for PTS during the acute phase of DVT.

ELIGIBILITY CRITERIA

We searched the PubMed and Cochrane databases for studies published between January 2000 and January 2021, including randomized clinical trials, meta-analyses, systematic reviews and observational studies.

RESULTS

Risk factors for PTS such as proximal location of DVT, obesity, chronic venous disease, history of DVT are associated with higher risk of PTS. On the initial ultrasound-Doppler, a high thrombotic burden appears to be a predictor of PTS. Among the evaluated biomarkers, some inflammatory markers such as ICAM-1, MMP-1 and MMP-8 appear to be associated with a higher risk of developing PTS. Coagulation disorders are not associated with risk of developing PTS. Role of endothelial biomarkers in predicting PTS has been poorly explored. Lastly, vitamin K antagonist was associated with a higher risk of developing PTS when compared to direct oral anticoagulants and low molecular weight heparin.

CONCLUSIONS

Several risk factors during the acute phase of VTE are associated with an increased risk of developing PTS. There is a high-unmet medical need to identify potential biomarkers for early detection of patients at risk of developing PTS after VTE. Inflammatory and endothelial biomarkers should be explored in larger prospective studies to identify populations that could benefit from new therapies.

Incidence and predictors of post-thrombotic syndrome in patients with proximal DVT in a real-world setting: findings from the GARFIELD-VTE registry

Although substantial progress has been made in the pathophysiology and management of the post-thrombotic syndrome (PTS), several aspects still need clarification. Among them, the incidence and severity of PTS in the real world, the risk factors for its development, the value of patient's self-evaluation, and the ability to identify patients at risk for severe PTS. Eligible participants (n = 1107) with proximal deep-vein thrombosis (DVT) from the global GARFIELD-VTE registry underwent conventional physician's evaluation for PTS 36 months after diagnosis of their DVT using the Villalta score. In addition, 856 patients completed a Villalta questionnaire at 24 months. Variable selection was performed using stepwise algorithm, and predictors of severe PTS were incorporated into a multivariable risk model. The optimistic adjusted c-index was calculated using bootstrapping techniques. Over 36-months, 27.8% of patients developed incident PTS (mild in 18.7%, moderate in 5.7%, severe in 3.4%). Patients with incident PTS were older, had a lower prevalence of transient risk factors of DVT and a higher prevalence of persistent risk factors of DVT. Self-assessment of overall PTS at 24 months showed an agreement of 63.4% with respect to physician's evaluations at 36 months. The severe PTS multivariable model provided an optimistic adjusted c-index of 0.68 (95% CI 0.59-0.77). Approximately a quarter of DVT patients experienced PTS over 36 months after VTE diagnosis. Patient's self-assessment after 24 months provided added value for estimating incident PTS over 36 months. Multivariable risk analysis allowed good discrimination for severe PTS.

A systematic review and meta-analysis of risk prediction models for post-thrombotic syndrome in patients with deep vein thrombosis

Objective

This systematic review and meta-analysis aimed to systematically evaluate the prediction models for the risk of post-thrombotic syndrome (PTS) in deep vein thrombosis (DVT) patients.

Methods

This systematic review and meta-analysis was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). A systematic search on the following electronic database: PubMed/MEDLINE, EMBASE, and Cochrane Library, and Chinese databases such as WANFANG and CNKI was conducted to look for relevant articles based on the research question. The risk of bias for each studies included was carried out based on Prediction Model Risk of Bias Assessment Tool (PROBAST).

Results

We identified 10 studies that developed a total of 13 clinical prediction models for PTS risk in DVT patients, 3 models were externally validated, 2 models were temporally validated. The top 5 predictors were: BMI (N = 9), Varicose vein (N = 6), Baseline Villalta Score (N = 6), Iliofemoral thrombosis (N = 5), and Age (N = 4). The high risk of bias was from the analysis domain, which the number of participants and selection of predictors often did not meet the requirements of PROBAST. A random-effects meta-analysis of C-statistics was conducted, the pooled discrimination was C-statistic 0.75, 95%CI (0.69, 0.81).

Conclusion

Among the 13 PTS risk prediction models reported in this study, no prediction model has been applied to clinical practice due to the lack of external validation. In the development of prediction models, most models were not standardized in data analysis. It is recommended that future studies on the design and implementation of prediction models refer to Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and PROBAST.

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.

Performance and Head-to-Head Comparison of Three Clinical Models to Predict Occurrence of Postthrombotic Syndrome: A Validation Study

OBJECTIVE

 The SOX-PTS, Amin, and Méan models are three different clinical prediction scores stratifying the risk for postthrombotic syndrome (PTS) development in patients with acute deep vein thrombosis (DVT) of the lower limbs. Herein, we aimed to assess and compare these scores in the same cohort of patients.

METHODS

 We retrospectively applied the three scores in a cohort of 181 patients (196 limbs) who participated in the SAVER pilot trial for an acute DVT. Patients were stratified into PTS risk groups using positivity thresholds for high-risk patients as proposed in the derivation studies. All patients were assessed for PTS 6 months after index DVT using the Villalta scale. We calculated the predictive accuracy for PTS and area under receiver operating characteristic (AUROC) curve for each model.

RESULTS

 The Méan model was the most sensitive (sensitivity 87.7%; 95% confidence interval [CI]: 77.2-94.5) with the highest negative predictive value (87.5%; 95% CI: 76.8-94.4) for PTS. The SOX-PTS was the most specific score (specificity 97.5%; 95% CI: 92.7-99.5) with the highest positive predictive value (72.7%; 95% CI: 39.0-94.0). The SOX-PTS and Méan models performed well for PTS prediction (AUROC: 0.72; 95% CI: 0.65-0.80 and 0.74; 95% CI: 0.67-0.82), whereas the Amin model did not (AUROC: 0.58; 95% CI: 0.49-0.67).

CONCLUSION

 Our data support that the SOX-PTS and Méan models have good accuracy to stratify the risk for PTS.

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.

The Post-thrombotic Syndrome-Prevention and Treatment: VAS-European Independent Foundation in Angiology/Vascular Medicine Position Paper

Importance

The post-thrombotic syndrome (PTS) is the most common long-term complication of deep vein thrombosis (DVT), occurring in up to 40–50% of cases. There are limited evidence-based approaches for PTS clinical management.

Objective

To provide an expert consensus for PTS diagnosis, prevention, and treatment.

Evidence-Review

MEDLINE, Cochrane Database review, and GOOGLE SCHOLAR were searched with the terms “post-thrombotic syndrome” and “post-phlebitic syndrome” used in titles and abstracts up to September 2020.

Filters Were

English, Controlled Clinical Trial / Systematic Review / Meta-Analysis / Guideline. The relevant literature regarding PTS diagnosis, prevention and treatment was reviewed and summarized by the evidence synthesis team. On the basis of this review, a panel of 15 practicing angiology/vascular medicine specialists assessed the appropriateness of several items regarding PTS management on a Likert-9 point scale, according to the RAND/UCLA method, with a two-round modified Delphi method.

Findings

The panelists rated the following as appropriate for diagnosis: 1-the Villalta scale; 2- pre-existing venous insufficiency evaluation; 3-assessment 3–6 months after diagnosis of iliofemoral or femoro-popliteal DVT, and afterwards periodically, according to a personalized schedule depending on the presence or absence of clinically relevant PTS. The items rated as appropriate for symptom relief and prevention were: 1- graduated compression stockings (GCS) or elastic bandages for symptomatic relief in acute DVT, either iliofemoral, popliteal or calf; 2-thigh-length GCS (30–40 mmHg at the ankle) after ilio-femoral DVT; 3- knee-length GCS (30–40 mmHg at the ankle) after popliteal DVT; 4-GCS for different length of times according to the severity of periodically assessed PTS; 5-catheter-directed thrombolysis, with or without mechanical thrombectomy, in patients with iliofemoral obstruction, severe symptoms, and low risk of bleeding. The items rated as appropriate for treatment were: 1- thigh-length GCS (30–40 mmHg at the ankle) after iliofemoral DVT; 2-compression therapy for ulcer treatment; 3- exercise training. The role of endovascular treatment (angioplasty and/or stenting) was rated as uncertain, but it could be considered for severe PTS only in case of stenosis or occlusion above the inguinal ligament, followed by oral anticoagulation.

Conclusions and Relevance

This position paper can help practicing clinicians in PTS management.