Inferior Vena Cava Edge Tracking Echocardiography: A Promising Tool with Applications in Multiple Clinical Settings

Prolapsing Intrigue: A Case of Superior Vena Cava Mass Visualized by Echocardiography From the Subcostal Window Unveiling an Anterior Mediastinal Type B2 Thymoma

Imaging the superior vena cava (SVC) during two-dimensional (2D) transthoracic echocardiographic examination is challenging and should be performed routinely. Here, we present a case where a lower (juxta-atrial) SVC mass was seen prolapsing into the right atrium by 2D transthoracic echocardiography; in this case, the imaging of the lower (juxta-atrial) SVC was done from the subcostal window. It was not possible to image the SVC from the suprasternal, right supraclavicular, left parasternal, or apical windows. CT scan of the chest with intravenous contrast was done in this case and showed an anterior mediastinal mass invading the SVC and prolapsing into the right atrium. CT-guided biopsy proved the mass to be a type B2 thymoma.

Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure

Current noninvasive estimation of right atrial pressure (RAP) by inferior vena cava (IVC) measurement during echocardiography may have significant inter-rater variability due to different levels of observers' experience. Therefore, there is a need to develop new approaches to decrease the variability of IVC analysis and RAP estimation. This study aims to develop a fully automated artificial intelligence (AI)-based system for automated IVC analysis and RAP estimation. We presented a multi-stage AI system to identify the IVC view, select good quality images, delineate the IVC region and quantify its thickness, enabling temporal tracking of its diameter and collapsibility changes. The automated system was trained and tested on expert manual IVC and RAP reference measurements obtained from 255 patients during routine clinical workflow. The performance was evaluated using Pearson correlation and Bland-Altman analysis for IVC values, as well as macro accuracy and chi-square test for RAP values. Our results show an excellent agreement (r=0.96) between automatically computed versus manually measured IVC values, and Bland-Altman analysis showed a small bias of [Formula: see text]0.33 mm. Further, there is an excellent agreement ([Formula: see text]) between automatically estimated versus manually derived RAP values with a macro accuracy of 0.85. The proposed AI-based system accurately quantified IVC diameter, collapsibility index, both are used for RAP estimation. This automated system could serve as a paradigm to perform IVC analysis in routine echocardiography and support various cardiac diagnostic applications.

Evaluation of the Caval aortic index in fetal growth restriction: A case-control study in a tertiary center

OBJECTIVE

To examine the inferior vena cava (IVC) diameter, aortic diameter, and caval aortic index in fetuses with growth restriction and to compare the findings with fetuses from uncomplicated pregnancies at similar gestational weeks.

METHODS

This prospective study was conducted with a total of 176 pregnant women. According to the diagnostic criteria, 84 pregnancies diagnosed with fetal growth restriction (FGR) were compared with a control group of 92 uncomplicated pregnancies at similar gestational weeks.

RESULTS

The aortic and IVC diameter values were significantly lower in the FGR group (P < 0.001), whereas the caval aortic index was similar between the two groups. The examination of the ultrasound parameters of the cases with FGR according to the neonatal intensive care requirement revealed similar aortic diameter and caval aortic index values but a significantly smaller IVC diameter in the cases requiring neonatal intensive care (P = 0.022).

CONCLUSION

We determined that the aortic and IVC diameter values were smaller in the fetuses with growth restriction, but the caval aortic index was similar in the two groups. The measurement of the IVC diameter in FGR may be useful in predicting the neonatal intensive care requirements of these fetuses.

Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications

The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.

Non-Invasive Estimation of Right Atrial Pressure Using a Semi-Automated Echocardiographic Tool for Inferior Vena Cava Edge-Tracking

The non-invasive estimation of right atrial pressure (RAP) would be a key advancement in several clinical scenarios, in which the knowledge of central venous filling pressure is vital for patients’ management. The echocardiographic estimation of RAP proposed by Guidelines, based on inferior vena cava (IVC) size and respirophasic collapsibility, is exposed to operator and patient dependent variability. We propose novel methods, based on semi-automated edge-tracking of IVC size and cardiac collapsibility (cardiac caval index—CCI), tested in a monocentric retrospective cohort of patients undergoing echocardiography and right heart catheterization (RHC) within 24 h in condition of clinical and therapeutic stability (170 patients, age 64 ± 14, male 45%, with pulmonary arterial hypertension, heart failure, valvular heart disease, dyspnea, or other pathologies). IVC size and CCI were integrated with other standard echocardiographic features, selected by backward feature selection and included in a linear model (LM) and a support vector machine (SVM), which were cross-validated. Three RAP classes (low < 5 mmHg, intermediate 5−10 mmHg and high > 10 mmHg) were generated and RHC values used as comparator. LM and SVM showed a higher accuracy than Guidelines (63%, 71%, and 61% for LM, SVM, and Guidelines, respectively), promoting the integration of IVC and echocardiographic features for an improved non-invasive estimation of RAP.