Dataset Information

Robot-assisted removal of inferior vena cava filter.

ABSTRACT: This case describes a 57-year-old morbidly obese woman with complications of a permanent Bard inferior vena cava (IVC) filter (BD Bard, Covington, Ga) placed in 2005. She complained of recurrent abdominal pain for 6 years associated with constipation, nausea, and bloating. Computed tomography venography showed an IVC filter with malposition, strut fracture, and extrusion, and a fractured strut protruded outside of the vena cava, adjacent to the distal right common iliac artery. The da Vinci Xi Surgical System (Intuitive Surgical, Sunnyvale, Calif) was used to remove the IVC filter and its displaced fractured strut. The operation time was 189 minutes, with an estimated blood loss of 200 mL. She was discharged home on postoperative day 2 with no complications. She had complete resolution of abdominal pain at her follow-up appointment.

SUBMITTER: Lin JC

PROVIDER: S-EPMC7298563 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robot-assisted removal of inferior vena cava filter.

Lin Judith C JC Patel Amit A Rogers Craig G CG

Journal of vascular surgery cases and innovative techniques 20200415 2

This case describes a 57-year-old morbidly obese woman with complications of a permanent Bard inferior vena cava (IVC) filter (BD Bard, Covington, Ga) placed in 2005. She complained of recurrent abdominal pain for 6 years associated with constipation, nausea, and bloating. Computed tomography venography showed an IVC filter with malposition, strut fracture, and extrusion, and a fractured strut protruded outside of the vena cava, adjacent to the distal right common iliac artery. The da Vinci Xi S ...[more]

PMID: 32566810

Similar Datasets

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.

Project description:Background: Previous observational studies suggest that inferior vena cava filter placement in pulmonary embolism patients complicated with congestive heart failure, mechanical ventilation, and shock may have a mortality benefit. We sought to analyze the survival benefits of inferior vena cava filter in pulmonary embolism patients complicated with acute myocardial infarction, acute respiratory failure, shock, or requiring treatment with thrombolytics. Methods: This retrospective observational study used hospital discharge data from the National Inpatient Sample Data (NIS). ICD-9-CM coding was used to identify complicated pulmonary embolism patients (N = 254,465) in NIS from 2002 to 2014, including the subgroups of acute myocardial infarction, acute respiratory failure, shock, and thrombolytics. Inferior vena cava filter recipients were 1:1 propensity score-matched on age, sex, race, deep vein thrombosis, Elixhauser comorbidities, and other pulmonary embolism comorbidities (45 covariates) to non-inferior vena cava filter recipients in complicated pulmonary embolism patients and separately in each subgroup. Clinical outcomes were compared between the inferior vena cava filter group and the non-inferior vena cava filter group. Results: Mortality rate in complicated pulmonary embolism patients with inferior vena cava filter placement was lower (20.9% vs. 33%; NNT = 8.28, 95% confidence interval (CI) 7.91-8.69, E-value = 2.53) and in the subgroups; acute myocardial infarction (17.9% vs. 30.1%; NNT = 8.19, 95% CI 7.52-8.92, E-value = 2.76), acute respiratory failure (19.5% vs. 29.7%; NNT = 9.76, 95% CI 8.67-11.16, E-value = 2.38), shock (30.7% vs. 47.1%; NNT = 6.08, 95% CI 5.73-6.47, E-value = 2.43), and with the use of thrombolytics (7% vs. 12.9 %; NNT 17.1, 95% CI 14.88-20.12, E-value = 3.01) (p < 0.001 for all). Conclusion: Inferior vena cava filter placement in pulmonary embolism complicated with acute myocardial infarction, acute respiratory failure, shock, or requiring thrombolytic therapy was associated with reduced mortality.

Dataset Information

Robot-assisted removal of inferior vena cava filter.

Publications

Robot-assisted removal of inferior vena cava filter.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets