Imaging of Deep Venous Pathology

Imaging plays an important role in the identification and assessment of clinically suspected venous pathology. The purpose of this article is to review the spectrum of image-based diagnostic tools used in the investigation of suspected deep vein disease, both obstructive (deep vein thrombosis and post-thrombotic vein changes) as well as insufficiency (e.g., compression syndromes and pelvic venous insufficiency). Additionally, specific imaging modalities are used for the treatment and during clinical follow-up. The use of duplex ultrasound, magnetic resonance venography, computed tomography venography and intravascular ultrasound as well as conventional venography will be discussed in this pictorial review.

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.

Persistently High Levels of Coagulation Factor XI as a Risk Factor for Venous Thrombosis

Coagulation factor XI (FXI) promotes fibrin formation and inhibits fibrinolysis. Elevated plasma FXI levels, limited to a single measurement, are associated with a higher thrombotic risk. Our case-control study aimed to identify the effect of persistently increased plasma FXI levels on the risk of deep vein thrombosis (DVT). All patients evaluated between January 2016 and January 2018 for a first episode of proximal DVT of the lower extremity were considered for enrolment. Plasma FXI levels were measured at least 1 month after the discontinuation of anticoagulant treatment (T1). The patients with increased plasma FXI levels (>90th percentile of controls) were tested again 3 months later (T2). Among the 200 enrolled patients (M/F 114/86, age range 26-87 years), 47 patients had increased plasma FXI levels at T1 and16 patients had persistently increased plasma FXI levels at T2. The adjusted odds ratio for DVT was 2.4 (95% CI, 1.3 to 5.5, p < 0.001) for patients with increased FXI levels at T1 and 5.2 (95% CI, 2.3 to 13.2, p < 0.001) for patients with persistently high FXI levels at T2. Elevated FXI levels constitute a risk factor for deep vein thrombosis, and this risk nearly doubled in patients with persistently increased plasma FXI levels. Larger prospective studies are needed to confirm our findings.

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.