Faculty Bibliography

BACKGROUND:The Bashir Endovascular Catheter (BEC) is a novel pharmaco-mechanical device designed to enhance thrombolysis by increasing the exposure of thrombus to endogenous and exogenous thrombolytics. The aim of this prospective, multicenter, single-arm study was to evaluate the feasibility and initial safety of the BEC in patients with acute intermediate-risk pulmonary embolism (PE). METHODS:Patients with symptomatic PE and right ventricular to left ventricular diameter ratio ≥0.9 as documented by computer tomography angiography were eligible for enrollment. The primary safety end points were device related death or adverse events, and major bleeding within 72 hours after BEC directed therapy. RESULTS:=0.0005; [95% CI, 5.5-13.4]; 37.1% reduction). CONCLUSIONS:In this early feasibility study of the BEC for intermediate-risk PE, there were no deaths or device-related adverse events and a significant reduction in right ventricular to left ventricular diameter ratio and thrombus burden. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03927508.

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG, and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

Severe acute respiratory syndrome coronavirus 2 is associated with a prothrombotic state in infected patients. After presenting a case of right ventricular thrombus in a patient with coronavirus disease-2019 (COVID-19), we discuss the unique challenges in the evaluation and treatment of COVID-19 patients, highlighting our COVID-19-modified pulmonary embolism response team algorithm. (Level of Difficulty: Beginner.).

This paper reports our study on the impact of transcatheter aortic valve replacement (TAVR) on the classification of aortic stenosis (AS) patients using cardio-mechanical modalities. Machine learning algorithms such as decision tree, random forest, and neural network were applied to conduct two tasks. Firstly, the pre- and post-TAVR data are evaluated with the classifiers trained in the literature. Secondly, new classifiers are trained to classify between pre- and post-TAVR data. Using analysis of variance, the features that are significantly different between pre- and post-TAVR patients are selected and compared to the features used in the pre-trained classifiers. The results suggest that pre-TAVR subjects could be classified as AS patients but post-TAVR could not be classified as healthy subjects. The features which differentiate pre- and post-TAVR patients reveal different distributions compared to the features that classify AS patients and healthy subjects. These results could guide future work in the classification of AS as well as the evaluation of the recovery status of patients after TAVR treatment.

OBJECTIVES:This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. METHODS:Experimental measurements were collected from twenty patients with AS and twenty healthy subjects. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. RESULTS:In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. CONCLUSION:The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz. SIGNIFICANCE:The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations.

We describe the case of a 72-year-old gentleman who was referred to our institution for management of cardiogenic shock from a massive pulmonary embolism. Right heart catheterization revealed a low cardiac index and markedly elevated pulmonary pressures, suggested long-standing venous thromboembolic (VTE) disease that evolved into chronic thromboembolic pulmonary hypertension (CTEPH). The patient was cannulated to veno-arterial extra-corporeal membrane oxygenation and eventually treated with pulmonary embolectomy and thromboendarterectomy. Subsequently discovered inferior vena cava clot and left iliac deep vein thrombosis were treated with suction and mechanical thrombectomy. Intravascular ultrasound of the left lower extremity venous system identified iliac vein compression syndrome (IVCS) as the culprit of the patient's VTE and CTEPH. A left iliac stent was placed and the patient was discharged on Warfarin for anticoagulation. The case illustrates the rapidly expanding armamentarium for VTE treatment and proposes IVCS as a new, potentially underrecognized risk factor for CTEPH.