Inferior Vena Cava Atresia: Characterisation of Risk Factors, Treatment, and Outcomes.

PURPOSE: To characterise (1) the risk factors associated with inferior vena cava (IVC) atresia, (2) the radiographic and clinical presentations of deep vein thrombosis (DVT) in patients with IVC atresia, and (3) the treatment and outcome of DVT in patients with IVC atresia. METHODS: The electronic medical record was systematically reviewed for thrombotic risk factors in patients who presented with lower-extremity DVT (n = 409) at a single centre between 1996 and 2017. Patients with IVC atresia were identified based on imaging and chart review. Differences in demographics and thrombotic risk factors between patients with and without IVC atresia were statistically assessed. Extent and chronicity of DVT on imaging, clinical presentation, treatment, and outcomes were evaluated for all patients with IVC atresia. RESULTS: 4.2% of DVT patients (17/409) were found to have IVC atresia; mean age at diagnosis was 25.5 ± 9.4 years. The rate of heritable thrombophilia was significantly higher in patients with IVC atresia compared to patients without IVC atresia (52.9% vs. 17.9%, p < 0.0001). There were bilateral DVT in 70.6% of IVC atresia patients; DVT was chronic in 41.2% and acute on chronic in 58.8%. Pre-intervention Villalta scores were 13.9 ± 9.8 in the left limb and 8.5 ± 7.0 in the right limb. DVT in IVC atresia patients was typically treated with catheter-directed thrombolysis followed by stent placement, achieving complete or partial symptom resolution in 78.6% of cases. CONCLUSION: Thrombotic risk factors such as heritable thrombophilia are associated with IVC atresia. IVC atresia patients can experience high burdens of lower-extremity thrombotic disease at a young age which benefit from endovascular treatment. LEVEL OF EVIDENCE: Level 4.

Advanced transcatheter aortic valve implantation (TAVI) planning from CT with ShapeForest.

Transcatheter aortic valve implantation (TAVI) is becoming a standard treatment for non-operable and high-risk patients with symptomatic severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, comprehensive preoperative planning is crucial for a successful outcome, with the most important decisions made during planning being the selection of the proper implant size, and determining the correct C-arm angulations. While geometric models extracted from 3D images are often used to derive these measurements, the complex shape variation of the AV anatomy found in these patients causes many of the shape representations used to estimate such geometric models to fail in capturing morphological characteristics in sufficient detail. In addition, most current approaches only model the aortic valve (AV), omitting modeling the left ventricle outflow tract (LVOT) entirely despite its high correlation with severe complications such as annulus ruptures, paravalvular leaks and myocardial infarction. We propose a fully automated method to extract patient specific models of the AV and the LVOT, and derive comprehensive biomarkers for accurate TAVI planning. We utilize a novel shape representation--the ShapeForest--which is able to model complex shape variation, preserves local shape information, and incorporates prior knowledge during shape space inference. Extensive quantitative and qualitative experiments performed on 630 volumetric data sets demonstrate an accuracy of 0.69 mm for the AV and 0.83 mm for the LVOT, an improvement of over 16% and 18% respectively when compared against state of the art methods.