Cavopulmonary assist: Long-term reversal of the Fontan paradox

Objective Fontan circulatory inefficiency can be addressed by replacing the missing subpulmonary power source to reverse the Fontan paradox. An implantable cavopulmonary assist device is described that will simultaneously reduce systemic venous pressure and increase pulmonary arterial pressure, improving preload and cardiac output, in a univentricular Fontan circulation on a long-term basis. Methods A rotary blood pump that was based on the von Karman viscous pump was designed for implantation into the total cavopulmonary connection (TCPC). It will impart modest pressure energy to augment Fontan flow without risk of obstruction. In the event of rotational failure, it is designed to default to a passive flow diverter. Pressure-flow performance was characterized in vitro in a Fontan mock circulatory loop with blood analog. Results The pump performed through the fully specified operating range, augmenting flow in all 4 directions of the TCPC. Pressure rise of 6 to 8 mm Hg was readily achieved, ranging to 14 mm Hg at highest speed (5600 rpm). Performance was consistent across a wide range of cardiac outputs. In stalled condition (0 rpm), there was no discernible pressure loss across the TCPC. Conclusions A blood pump technology is described that can reverse the Fontan paradox and may permit a surgical strategy of long-term biventricular maintenance of a univentricular Fontan circulation. The technology is intended for Fontan failure in which right-sided circulatory inefficiencies predominate and ventricular systolic function is preserved. It may also apply before clinical Fontan failure as health maintenance to preempt the progression of Fontan disease

A CAD System for the Early Prediction of Hypertension based on Changes in Cerebral Vasculature

© 2019 IEEE. Hypertension is a leading cause for mortality in the US and a significant contributor to many vascular and non vascular diseases. Previous literature reports suggest that specific cerebral vascular alterations precede the onset of hypertension. In this manuscript, we propose a magnetic resonance angiography (MRA)-based computer-aided-diagnosis (CAD) system for the early detection of hypertension. The steps of the proposed CAD system are: 1) preprocessing of the MRA input data to correct the bias resulting from the magnetic field, remove noise effects, reduce contrast non-uniformities, enhance homogeneity using a generalized Gauss-Markov random field (GGMRF), and normalize data to enhance the segmentation process, 2) delineating the cerebral vasculature using a deep 3-D convolutional neural network (CNN) automatically and accurately, 3) extraction of vascular features (cerebrovascular diameters and tortuosity) that are reported to change with the progression of hypertension and constructing the feature vectors, 4) using the feature vectors for classifying input data using a support vector machine (SVM) classifier. We report a 90% classification accuracy in distinguishing between normal and potential hypertensive subjects. These results demonstrate the efficacy of using the proposed vascular features to predict pre-hypertension or hypertension. Clinicians could track the alterations of these vascular features over time for people at risk of developing hypertension for optimal medical management and mitigate adverse events

Bovine Model of Doxorubicin-Induced Cardiomyopathy

Left ventricular assist devices (LVADs) constitute a recent advance in heart failure (HF) therapeutics. As the rigorous experimental assessment of LVADs in HF requires large animal models, our objective was to develop a bovine model of cardiomyopathy. Male calves (n = 8) were used. Four animals received 1.2 mg/kg intravenous doxorubicin weekly for seven weeks and four separate animals were studied as controls. Doxorubicin-treated animals were followed with weekly echocardiography. Target LV dysfunction was defined as an ejection fraction ≤35%. Sixty days after initiating doxorubicin, a terminal study was performed to determine hemodynamic, histological, biochemical, and molecular parameters. All four doxorubicin-treated animals exhibited significant (P < 0.05) contractile dysfunction, with target LV dysfunction achieved in three animals. Doxorubicin-treated hearts exhibited significantly reduced coronary blood flow and interstitial fibrosis and significantly increased apoptosis and myocyte size. Gene expression of atrial natriuretic factor increased more than 3-fold. Plasma norepinephrine and epinephrine levels were significantly increased early and late during the development of cardiomyopathy, respectively. We conclude that sequential administration of intravenous doxorubicin in calves induces a cardiomyopathy with many phenotypic hallmarks of the failing human heart. This clinically-relevant model may be useful for testing pathophysiologic responses to LVADs in the context of HF

Cavopulmonary assist for the failing Fontan circulation: impact of ventricular function on mechanical support strategy

Mechanical circulatory support--either ventricular assist device (VAD, left-sided systemic support) or cavopulmonary assist device (CPAD, right-sided support)--has been suggested as treatment for Fontan failure. The selection of left- versus right-sided support for failing Fontan has not been previously defined. Computer simulation and mock circulation models of pediatric Fontan patients (15-25 kg) with diastolic, systolic, and combined systolic and diastolic dysfunction were developed. The global circulatory response to assisted Fontan flow using VAD (HeartWare HVAD, Miami Lakes, FL) support, CPAD (Viscous Impeller Pump, Indianapolis, IN) support, and combined VAD and CPAD support was evaluated. Cavopulmonary assist improves failing Fontan circulation during diastolic dysfunction but preserved systolic function. In the presence of systolic dysfunction and elevated ventricular end-diastolic pressure (VEDP), VAD support augments cardiac output and diminishes VEDP, while increased preload with cavopulmonary assist may worsen circulatory status. Fontan circulation can be stabilized to biventricular values with modest cavopulmonary assist during diastolic dysfunction. Systemic VAD support may be preferable to maintain systemic output during systolic dysfunction. Both systemic and cavopulmonary support may provide best outcome during combined systolic and diastolic dysfunction. These findings may be useful to guide clinical cavopulmonary assist strategies in failing Fontan circulations

Transapical miniaturized ventricular assist device: Design and initial testing

BackgroundLeft ventricular assist devices are increasingly used to treat patients with advanced and otherwise refractory heart failure as bridge to transplant or destination therapy. We evaluated a new miniaturized left ventricular assist device that requires minimal surgery for implantation, potentially allowing implantation in earlier stage heart failure.MethodsHeartWare (Miami Lakes, Fla) developed transapical miniaturized ventricular assist device. Acute (n = 4), 1-week (n = 2), and 30-day (n = 4) bovine model experiments evaluated hemodynamic efficacy and biocompatibility of the device, which was implanted through small left thoracotomy with single insertion at apex of left ventricle without cardiopulmonary bypass. The device outflow cannula was positioned across the aortic valve. The international normalized ratio was maintained between 2.0 and 2.5 with warfarin. Hemodynamic, echocardiographic, fluoroscopic, hematologic, and blood chemistry measurements were evaluated.ResultsThe device was successfully implanted through the left ventricular apex in all 10 animals. The device was operated at 15,000 ± 1000 rpm (power consumption, 3.5–6.0 W). The device maintained normal end-organ perfusion with no significant hemolysis (0–30 mg/dL). There were no pump failures or device-related complications. At autopsy, no abnormalities were seen in endocardium, aortic valve leaflets, or aortic root. There was no evidence of thromboembolism or abnormalities in any peripheral end organs.ConclusionsWe successfully demonstrated feasibility of a novel intraventricular assist device that can be completely implanted through left ventricular apex. This transapical surgical approach eliminates needs for sternotomy, device pocket, cardiopulmonary bypass, ventricular coring, and construction of an outflow graft anastomosis

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

© 2019 The Authors Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events

10 A noninvasive approach for the early detection of diabetic retinopathy

This chapter introduces one of the most critical problems in ophthalmology, specifically the diagnosis and detection of diabetic retinopathy (DR). Developing a fast, accurate, and reliable method for the early detection of DR is of great clinical importance to prevent blindness in patients. For this reason, various methods for early detection of DR have been investigated and used such as a dilated eye examination, tonometry, fluorescein angiography, optical coherence tomography, and ultrawide-field retinal imaging. With the increased popularity of machine learning, researchers have formulated their own algorithms and methods to detect DR with various rates of success. This chapter overviews past and current diagnostic methods that have been developed for DR. In addition, this chapter addresses new methodologies being developed/researched and some challenges that researchers face in developing fast, accurate, and reliable diagnosis

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Special Issue “Computer Aided Diagnosis Sensors”

Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors [...