Dataset Information

Successful endovascular retrieval of an inferior vena cava filter penetrating into aorta.

ABSTRACT: The inferior vena cava (IVC) filter is an approved and effective device for prevention of pulmonary embolism. Despite declared effectiveness in prevention of pulmonary embolism, certain IVC filter-related complications have been described. This case report deals with successful endovascular retrieval of an IVC filter penetrating into the aorta.

Learning objective

The use of inferior vena cava (IVC) filters has been associated with controversy in recent years, largely owing to concerns about the overuse. The perforation of IVC wall and further penetration of IVC filter struts into surrounding tissues belong to the most severe complication. The purpose of this report is to highlight potential severe complications associated with the use of IVC filter and to present the reader how the IVC filter-related complications can be successfully managed by endovascular treatment.

SUBMITTER: Hudak M

PROVIDER: S-EPMC10730273 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Successful endovascular retrieval of an inferior vena cava filter penetrating into aorta.

Hudák Marek M Koščo Martin M Rašiová Mária M

Journal of cardiology cases 20230816 6

The inferior vena cava (IVC) filter is an approved and effective device for prevention of pulmonary embolism. Despite declared effectiveness in prevention of pulmonary embolism, certain IVC filter-related complications have been described. This case report deals with successful endovascular retrieval of an IVC filter penetrating into the aorta.<h4>Learning objective</h4>The use of inferior vena cava (IVC) filters has been associated with controversy in recent years, largely owing to concerns abo ...[more]

PMID: 38126054

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Project description:ObjectiveInferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data.MethodsThe Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement.ResultsOverall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups.ConclusionsWe developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes.

Dataset Information

Successful endovascular retrieval of an inferior vena cava filter penetrating into aorta.

Learning objective

Publications

Successful endovascular retrieval of an inferior vena cava filter penetrating into aorta.

Similar Datasets

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