Search

Patients with a Ventricular Assist Device (VAD) are hemodynamically stable but show an impaired exercise capacity. Aim of this work is to identify and to describe the limiting factors of exercise physiology with a VAD. We searched for data concerning exercise in heart failure condition and after VAD implantation from the literature. Data were analyzed by using a cardiorespiratory simulator that worked as a collector of inputs coming from different papers. As a preliminary step the simulator was used to reproduce the evolution of hemodynamics from rest to peak exercise (ergometer cycling) in heart failure condition. Results evidence an increase of cardiac output of +2.8 l/min and a heart rate increase to 67% of the expected value. Then, we simulated the effect of a continuous-flow VAD at both rest and exercise. Total cardiac output increases of +3.0 l/min (+0.9 l/min due to the VAD and +2.1 l/min to the native ventricle). Since the left ventricle works in a non-linear portion of the diastolic stiffness line, we observed a consistent increase of pulmonary capillary wedge pressure (from 14 to 20 mmHg) for a relatively small increase of end-diastolic volume (from 182 to 189 cm3). We finally increased VAD speed during exercise to the maximum possible value and we observed a reduction of wedge pressure (-4.5 mmHg), a slight improvement of cardiac output (8.0 l/min) and a complete unloading of the native ventricle. The VAD can assure a proper hemodynamics at rest, but provides an insufficient unloading of the left ventricle and does not prevent wedge pressure from rising during exercise. Neither the VAD provides major benefits during exercise in terms of total cardiac output, which increases to a similar extend to an unassisted heart failure condition. VAD speed modulation can contribute to better unload the ventricle but the maximal flow reachable with the current devices is below the cardiac output observed in a healthy heart. [ABSTRACT FROM AUTHOR]

In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710±1.900% and 3.440±1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of-concept implementation is presented as an annex to this paper. [ABSTRACT FROM AUTHOR]

Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population’s structure. We also show that if an individual is classified to the wrong population, his/her disease risk can be systematically incorrectly estimated. [ABSTRACT FROM AUTHOR]

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is one of the leading causes of death worldwide, in particular myocardial dysfunction, which may lead to heart failure eventually. Understanding the electro-mechanics of the heart will help in developing more effective clinical treatments. In this paper, we present a multi-scale electro-mechanics model of the left ventricle (LV). The Holzapfel-Ogden constitutive law was used to describe the passive myocardial response in tissue level, a modified Grandi-Pasqualini-Bers model was adopted to model calcium dynamics in individual myocytes, and the active tension was described using the Niederer-Hunter-Smith myofilament model. We first studied the electro-mechanics coupling in a single myocyte in the healthy and diseased left ventricle, and then the single cell model was embedded in a dynamic LV model to investigate the compensation mechanism of LV pump function due to myocardial dysfunction caused by abnormality in cellular calcium dynamics. The multi-scale LV model was solved using an in-house developed hybrid immersed boundary method with finite element extension. The predictions of the healthy LV model agreed well with the clinical measurements and other studies, and likewise, the results in the failing states were also consistent with clinical observations. In particular, we found that a low level of intracellular Ca2+ transient in myocytes can result in LV pump function failure even with increased myocardial contractility, decreased systolic blood pressure, and increased diastolic filling pressure, even though they will increase LV stroke volume. Our work suggested that treatments targeted at increased contractility and lowering the systolic blood pressure alone are not sufficient in preventing LV pump dysfunction, restoring a balanced physiological Ca2+ handling mechanism is necessary. [ABSTRACT FROM AUTHOR]

Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders. [ABSTRACT FROM AUTHOR]

This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle) in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of “loss of controllability”, change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR) and stressor level lever (in the case of autonomous riding). Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term) and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001). Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7). Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given. [ABSTRACT FROM AUTHOR]

Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time. [ABSTRACT FROM AUTHOR]

Background: The restitution of the action potential duration (APDR) and conduction velocity (CVR) are mechanisms whereby cardiac excitation and repolarization adapt to changes in heart rate. They modulate the vulnerability to dangerous arrhythmia, but the mechanistic link between restitution and arrhythmogenesis remains only partially understood. Methods: This paper provides an experimental and theoretical study of repolarization and excitation restitution properties and their interactions in the intact human epicardium. The interdependence between excitation and repolarization dynamic is studied in 8 patients (14 restitution protocols, 1722 restitution curves) undergoing global epicardial mapping with multi-electrode socks before open heart surgery. A mathematical description of the contribution of both repolarization and conduction dynamics to the steepness of the APDR slope is proposed. Results: This study demonstrates that the APDR slope is a function of both activation and repolarization dynamics. At short cycle length, conduction delay significantly increases the APDR slope by interacting with the diastolic interval. As predicted by the proposed mathematical formulation, the APDR slope was more sensitive to activation time prolongation than to the simultaneous shortening of repolarization time. A steep APDR slope was frequently identified, with 61% of all cardiac sites exhibiting an APDR slope > 1, suggesting that a slope > 1 may not necessarily promote electrical instability in the human epicardium. APDR slope did not change for different activation or repolarization times, and it was not a function of local baseline APD. However, it was affected by the spatial organization of electrical excitation, suggesting that in tissue APDR is not a unique function of local electrophysiological properties. Spatial heterogeneity in both activation and repolarization restitution contributed to the increase in the modulated dispersion of repolarization, which for short cycle length was as high as 250 ms. Heterogeneity in conduction velocity restitution can translate into both activation and repolarization dispersion and increase cardiac instability. The proposed mathematical formulation shows an excellent agreement with the experimental data (correlation coefficient r = 0.94) and provides a useful tool for the understanding of the complex interactions between activation and repolarization restitution properties as well as between their measurements. [ABSTRACT FROM AUTHOR]

As the enterprise of the “Internet of Things” is rapidly gaining widespread acceptance, sensors are being deployed in an unrestrained manner around the world to make efficient use of this new technological evolution. A recent survey has shown that sensor deployments over the past decade have increased significantly and has predicted an upsurge in the future growth rate. In health-care services, for instance, sensors are used as a key technology to enable Internet of Things oriented health-care monitoring systems. In this paper, we have proposed a two-stage fundamental approach to facilitate the implementation of such a system. In the first stage, sensors promptly gather together the particle measurements of an android application. Then, in the second stage, the collected data are sent over a Femto-LTE network following a new scheduling technique. The proposed scheduling strategy is used to send the data according to the application’s priority. The efficiency of the proposed technique is demonstrated by comparing it with that of well-known algorithms, namely, proportional fairness and exponential proportional fairness. [ABSTRACT FROM AUTHOR]

In recent years the role of FSI (fluid-structure interaction) simulations in the analysis of the fluid-mechanics of heart valves is becoming more and more important, being able to capture the interaction between the blood and both the surrounding biological tissues and the valve itself. When setting up an FSI simulation, several choices have to be made to select the most suitable approach for the case of interest: in particular, to simulate flexible leaflet cardiac valves, the type of discretization of the fluid domain is crucial, which can be described with an ALE (Arbitrary Lagrangian-Eulerian) or an Eulerian formulation. The majority of the reported 3D heart valve FSI simulations are performed with the Eulerian formulation, allowing for large deformations of the domains without compromising the quality of the fluid grid. Nevertheless, it is known that the ALE-FSI approach guarantees more accurate results at the interface between the solid and the fluid. The goal of this paper is to describe the same aortic valve model in the two cases, comparing the performances of an ALE-based FSI solution and an Eulerian-based FSI approach. After a first simplified 2D case, the aortic geometry was considered in a full 3D set-up. The model was kept as similar as possible in the two settings, to better compare the simulations’ outcomes. Although for the 2D case the differences were unsubstantial, in our experience the performance of a full 3D ALE-FSI simulation was significantly limited by the technical problems and requirements inherent to the ALE formulation, mainly related to the mesh motion and deformation of the fluid domain. As a secondary outcome of this work, it is important to point out that the choice of the solver also influenced the reliability of the final results. [ABSTRACT FROM AUTHOR]

Background: The main behaviour change intervention available for coronary heart disease (CHD) patients is cardiac rehabilitation. There is little recognition of what the active ingredients of behavioural interventions for CHD might be. Using a behaviour change technique (BCT) framework to code existing interventions may help to identify this. The objectives of this systematic review are to determine the effectiveness of CHD behaviour change interventions and how this may be explained by BCT content and structure. Methods and Findings: A systematic search of Medline, EMBASE and PsycInfo electronic databases was conducted over a twelve year period (2003–2015) to identify studies which reported on behaviour change interventions for CHD patients. The content of the behaviour change interventions was coded using the Coventry Aberdeen and London—Refined (CALO-RE) taxonomy. Meta-regression analyses examined the BCT content as a predictor of mortality. Twenty two papers met the criteria for this review, reporting data on 16,766 participants. The most commonly included BCTs were providing information, and goal setting. There was a small but significant effect of the interventions on smoking (risk ratio (RR) = 0.89, 95% CI 0.81–0.97). The interventions did not reduce the risk of CHD events (RR = 0.86, 95% CI 0.68, 1.09), but significantly reduced the risk of mortality (RR = 0.82, 95% CI 0.69, 0.97). Sensitivity analyses did not find that any of the BCT variables predicted mortality and the number of BCTs included in an intervention was not associated with mortality (β = -0.02, 95% CI -0.06–0.03). Conclusions: Behaviour change interventions for CHD patients appear to have a positive impact on a number of outcomes. Using an existing BCT taxonomy to code the interventions helped us to understand which were the most commonly used techniques, providing information and goal setting, but not the active components of these complex interventions. [ABSTRACT FROM AUTHOR]

Some smartphones have the capability to process video streams from both the front- and rear-facing cameras simultaneously. This paper proposes a new monitoring method for simultaneous estimation of heart and breathing rates using dual cameras of a smartphone. The proposed approach estimates heart rates using a rear-facing camera, while at the same time breathing rates are estimated using a non-contact front-facing camera. For heart rate estimation, a simple application protocol is used to analyze the varying color signals of a fingertip placed in contact with the rear camera. The breathing rate is estimated from non-contact video recordings from both chest and abdominal motions. Reference breathing rates were measured by a respiration belt placed around the chest and abdomen of a subject; reference heart rates (HR) were determined using the standard electrocardiogram. An automated selection of either the chest or abdominal video signal was determined by choosing the signal with a greater autocorrelation value. The breathing rate was then determined by selecting the dominant peak in the power spectrum. To evaluate the performance of the proposed methods, data were collected from 11 healthy subjects. The breathing ranges spanned both low and high frequencies (6–60 breaths/min), and the results show that the average median errors from the reflectance imaging on the chest and the abdominal walls based on choosing the maximum spectral peak were 1.43% and 1.62%, respectively. Similarly, HR estimates were also found to be accurate. [ABSTRACT FROM AUTHOR]

Objectives: It can be argued that with several players marketing a large number of brands, the pharmaceutical market in India is competitive. However, the pharmaceutical market should not be studied as a single market but, as a sum total of a large number of individual sub-markets. This paper examines the methodological issues with respect to defining the relevant market involved in studying concentration in the pharmaceutical market in India. Further, we have examined whether the Indian pharmaceutical market is competitive. Methods: Indian pharmaceutical market was studied using PharmaTrac, the sales audit data from AIOCD-AWACS, that organises formulations into 5 levels of therapeutic classification based on the EphMRA system. The Herfindahl-Hirschman Index (HHI) was used as the indicator of market concentration. We calculated HHI for the entire pharmaceutical market studied as a single market as well as at the five different levels of therapeutic classification. Results and Discussion: Whereas the entire pharmaceutical market taken together as a single market displayed low concentration (HHI = 226.63), it was observed that if each formulation is defined as an individual sub-market, about 69 percent of the total market in terms of market value displayed at least moderate concentration. Market should be defined taking into account the ease of substitutability. Since, patients cannot themselves substitute the formulation prescribed by the doctor with another formulation with the same indication and therapeutic effect, owing to information asymmetry, it is appropriate to study market concentration at the narrower levels of therapeutic classification. [ABSTRACT FROM AUTHOR]

To better understand the interaction of physical activity and air pollution exposure, it is important to quantify the change in ventilation rate incurred by activity. In this paper, we describe a method for estimating ventilation using easily-measured variables such as heart rate (HR), breathing rate (fB), and forced vital capacity (FVC). We recruited healthy adolescents to use a treadmill while we continuously measured HR, fB, and the tidal volume (VT) of each breath. Participants began at rest then walked and ran at increasing speed until HR was 160–180 beats per minute followed by a cool down period. The novel feature of this method is that minute ventilation () was normalized by FVC. We used general linear mixed models with a random effect for subject and identified nine potential predictor variables that influence either or FVC. We assessed predictive performance with a five-fold cross-validation procedure. We used a brute force selection process to identify the best performing models based on cross-validation percent error, the Akaike Information Criterion and the p-value of parameter estimates. We found a two-predictor model including HR and fB to have the best predictive performance (/FVC = -4.247+0.0595HR+0.226fB, mean percent error = 8.1±29%); however, given the ubiquity of HR measurements, a one-predictor model including HR may also be useful (/FVC = -3.859+0.101HR, mean percent error = 11.3±36%). [ABSTRACT FROM AUTHOR]

Background: Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. Methods and Results: State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. Conclusion: This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation. [ABSTRACT FROM AUTHOR]

Introduction: The American Heart Association (AHA)/American College of Cardiology (ACC) guidelines for the classification of heart failure (HF) are descriptive but lack precise and objective measures which would assist in categorising such patients. Our aim was two fold, firstly to demonstrate quantitatively the progression of HF through each stage using a meta-analysis of existing left ventricular (LV) pressure-volume (PV) loop data and secondly use the LV PV loop data to create stage specific HF models. Methods and Results: A literature search yielded 31 papers with PV data, representing over 200 patients in different stages of HF. The raw pressure and volume data were extracted from the papers using a digitising software package and the means were calculated. The data demonstrated that, as HF progressed, stroke volume (SV), ejection fraction (EF%) decreased while LV volumes increased. A 2-element lumped parameter model was employed to model the mean loops and the error was calculated between the loops, demonstrating close fit between the loops. The only parameter that was consistently and statistically different across all the stages was the elastance (Emax). Conclusions: For the first time, the authors have created a visual and quantitative representation of the AHA/ACC stages of LVSD-HF, from normal to end-stage. The study demonstrates that robust, load-independent and reproducible parameters, such as elastance, can be used to categorise and model HF, complementing the existing classification. The modelled PV loops establish previously unknown physiological parameters for each AHA/ACC stage of LVSD-HF, such as LV elastance and highlight that it this parameter alone, in lumped parameter models, that determines the severity of HF. Such information will enable cardiovascular modellers with an interest in HF, to create more accurate models of the heart as it fails. [ABSTRACT FROM AUTHOR]

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. [ABSTRACT FROM AUTHOR]

Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and end-systolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects. [ABSTRACT FROM AUTHOR]

A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different approaches to evaluate transfer entropy, some of them already proposed, some novel, and present their implementation in a freeware MATLAB toolbox. Applications to simulated and real data are presented. [ABSTRACT FROM AUTHOR]

Objective: Heart failure (HF) is an important covariate and outcome in studies of elderly populations and cardiovascular disease cohorts, among others. Administrative data is increasingly being used for long-term clinical research in these populations. We aimed to conduct the first systematic review and meta-analysis of studies reporting on the validity of diagnostic codes for identifying HF in administrative data. Methods: MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify HF; or (b) Evaluating the validity of HF codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value [PPV], negative predictive value, or Kappa scores) for HF, or data sufficient for their calculation. Additional articles were located by hand search (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Using a random-effects model, pooled sensitivity and specificity values were produced, along with estimates of the positive (LR+) and negative (LR−) likelihood ratios, and diagnostic odds ratios (DOR = LR+/LR−) of HF codes. Results: Nineteen studies published from1999–2009 were included in the qualitative review. Specificity was ≥95% in all studies and PPV was ≥87% in the majority, but sensitivity was lower (≥69% in ≥50% of studies). In a meta-analysis of the 11 studies reporting sensitivity and specificity values, the pooled sensitivity was 75.3% (95% CI: 74.7–75.9) and specificity was 96.8% (95% CI: 96.8–96.9). The pooled LR+ was 51.9 (20.5–131.6), the LR− was 0.27 (0.20–0.37), and the DOR was 186.5 (96.8–359.2). Conclusions: While most HF diagnoses in administrative databases do correspond to true HF cases, about one-quarter of HF cases are not captured. The use of broader search parameters, along with laboratory and prescription medication data, may help identify more cases. [ABSTRACT FROM AUTHOR]

In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion. [ABSTRACT FROM AUTHOR]

Objective: Characterisation of anticoagulant control is fundamental to investigations of its association with clinical outcome. Anticoagulant control depends on several factors. This paper aims to illustrate the implications of different methods for measuring and analysing anticoagulant control in patients with second generation mechanical heart valve prostheses. Methods: International normalised ratio (INR) data collected during the 10-year follow-up of a randomised controlled trial were analysed. We considered the influence of: 3 different target INR ranges; anticoagulant control expressed as the proportion of INR readings (PoR) vs. anticoagulant control follow-up time (PoT); 3 ways of describing the profile of anticoagulant control over time. Results: Different target INR ranges dramatically influenced derived measures of anticoagulant control; the PoT within the target range varied from 88% for the widest to 28% for narrowest range. Overall distributions of PoR and PoT observations were similar but differed by up to ±20% for individuals; PoT exceeded PoR when control was good but was less than PoR when control was poor. Classifying PoT outside the target range showed that widely varying combinations of PoT too high and too low are possible across individuals. Conclusions: Researchers' choices about methods for measuring and quantifying anticoagulant control markedly influence the values derived from INR readings. The use of different methods across studies makes it difficult or impossible to compare findings and to establish an evidence base for clinical practice. Methods for quantifying anticoagulant control should be standardised. [ABSTRACT FROM AUTHOR]

Objective: To explore whether red yeast rice is a safe and effective alternative approach for dyslipidemia. Methods: Pubmed, the Cochrane Library, EBSCO host, Chinese VIP Information (VIP), China National Knowledge Infrastructure (CNKI), Wanfang Databases were searched for appropriate articles. Randomized trials of RYR (not including Xuezhikang and Zhibituo) and placebo as control in patients with dyslipidemia were considered. Two authors read all papers and independently extracted all relevant information. The primary outcomes were serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C). The secondary outcomes were increased levels of alanine transaminase, aspartate aminotransferase, creatine kinase, creatinine and fasting blood glucose. Results: A total of 13 randomized, placebo-controlled trials containing 804 participants were analyzed. Red yeast rice exhibited significant lowering effects on serum TC [WMD = −0.97 (95% CI: −1.13, −0.80) mmol/L, P<0.001], TG [WMD = −0.23 (95% CI: −0.31, −0.14) mmol/L, P<0.001], and LDL-C [WMD = −0.87 (95% CI: −1.03, −0.71) mmol/L, P<0.001] but no significant increasing effect on HDL-C [WMD = 0.08 (95% CI: −0.02, 0.19) mmol/L, P = 0.11] compared with placebo. No serious side effects were reported in all trials. Conclusions: The meta-analysis suggests that red yeast rice is an effective and relatively safe approach for dyslipidemia. However, further long-term, rigorously designed randomized controlled trials are still warranted before red yeast rice could be recommended to patients with dyslipidemia, especially as an alternative to statins. [ABSTRACT FROM AUTHOR]

Background and Purpose: Spontaneous intracerebral hemorrhage (ICH) is a devastating form of stroke with a poor prognosis overall. We conducted a systematic review and meta-analysis to identify and describe factors associated with early neurologic deterioration (END) after ICH. Methods: We sought to identify any factor which could be prognostic in the absence of an intervention. The Cochrane Library, EMBASE, the Global Health Library, and PubMed were searched for primary studies from the years 1966 to 2012 with no restrictions on language or study design. Studies of patients who received a surgical intervention or specific experimental therapies were excluded. END was defined as death, or worsening on a reliable outcome scale within seven days after onset. Results: 7,172 abstracts were reviewed, 1,579 full-text papers were obtained and screened. 14 studies were identified; including 2088 patients. Indices of ICH severity such as ICH volume (univariate combined OR per ml:1.37, 95%CI: 1.12–1.68), presence of intraventricular hemorrhage (2.95, 95%CI: 1.57–5.55), glucose concentration (per mmol/l: 2.14, 95%CI: 1.03–4.47), fibrinogen concentration (per g/l: 1.83, 95%CI: 1.03–3.25), and d-dimer concentration at hospital admission (per mg/l: 4.19, 95%CI: 1.88–9.34) were significantly associated with END after random-effects analyses. Whereas commonly described risk factors for ICH progression such as blood pressure, history of hypertension, and ICH growth were not. Conclusions: This study summarizes the evidence to date on early ICH prognosis and highlights that the amount and distribution of the initial bleed at hospital admission may be the most important factors to consider when predicting early clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Italy has a population of 60 million and a universal coverage single-payer healthcare system, which mandates collection of healthcare administrative data in a uniform fashion throughout the country. On the other hand, organization of the health system takes place at the regional level, and local initiatives generate natural experiments. This is happening in particular in primary care, due to the need to face the growing burden of chronic diseases. Health services research can compare and evaluate local initiatives on the basis of the common healthcare administrative data.However reliability of such data in this context needs to be assessed, especially when comparing different regions of the country. In this paper we investigated the validity of healthcare administrative databases to compute indicators of compliance with standards of care for diabetes, ischaemic heart disease (IHD) and heart failure (HF). Methods: We compared indicators estimated from healthcare administrative data collected by Local Health Authorities in five Italian regions with corresponding estimates from clinical data collected by General Practitioners (GPs). Four indicators of diagnostic follow-up (two for diabetes, one for IHD and one for HF) and four indicators of appropriate therapy (two each for IHD and HF) were considered. Results: Agreement between the two data sources was very good, except for indicators of laboratory diagnostic follow-up in one region and for the indicator of bioimaging diagnostic follow-up in all regions, where measurement with administrative data underestimated quality. Conclusion: According to evidence presented in this study, estimating compliance with standards of care for diabetes, ischaemic heart disease and heart failure from healthcare databases is likely to produce reliable results, even though completeness of data on diagnostic procedures should be assessed first. Performing studies comparing regions using such indicators as outcomes is a promising development with potential to improve quality governance in the Italian healthcare system. [ABSTRACT FROM AUTHOR]

Coronary collateral growth (arteriogenesis) is a physiological adaptive response to transient and repetitive occlusion of major coronary arteries in which small arterioles (native collaterals) with minimal to no blood flow remodel into larger conduit arteries capable of supplying adequate perfusion to tissue distal to the site of occlusion. The ability to reliably and reproducibly mimic transient, repetitive coronary artery occlusion (ischemia) in animal models is critical to the development of therapies to restore coronary collateral development in type II diabetes and the metabolic syndrome. Current animal models for repetitive coronary artery occlusion implement a pneumatic occluder (balloon) that is secured onto the surface of the heart with the suture, which is inflated manually, via a catheter connected to syringe, to effect occlusion of the left anterior descending coronary artery (LAD). This method, although effective, presents complications in terms of reproducibility and practicality. To address these limitations, we have designed a device for automated, transient inflation of balloon catheters in coronary artery occlusion models. This device allows repeated, consistent inflation (to either specified pressure or volume) and the capability for implementing very complex, month-long protocols. This system has significantly increased the reproducibility of coronary collateral growth studies in our laboratory, resulting in a significant decrease in the numbers of animals needed to complete each study while relieving laboratory personnel from the burden of extra working hours and enabling us to continue studies over periods when we previously could not. In this paper, we present all details necessary for construction and operation of the inflator. In addition, all of the components for this device are commercially available and economical (Table S1). It is our hope that the adoption of automated balloon catheter inflation protocols will improve the experimental reliability of transient ischemia studies at many research institutions. [ABSTRACT FROM AUTHOR]

Haemodynamic changes in cerebral circulation are associated with the natural ageing process and associated pathology, leading to the development of incapacitating neurological and neurovascular diseases. Due to inherent biological limitations, current literatures mostly aimed at studying the correlation descriptively or quantifying the relationship in vitro or using computational models. In this paper, a model of a carotid-jugular fistula in the rat was used to create a haemodynamic insult to the intracranial arterial circulation and subsequent venous drainage. An arterial-venous (AV) fistula was created in 12 rats, 6 of which are normotensive Wistar-Kyoto strain (WKY) and the rest spontaneously hypertensive strain (SHR) with an additional 6 in each strains designed as controls without previous surgery. After 4 weeks of convalescence, all 24 rats were euthanised and their cerebral circulation was examined histomorphologically. We confirmed an intrinsic morphological difference between normotensive WKY and hypertensive SHR and found a modest but significant arterial shrinkage in both strains induced with AV fistula. We also reported that alterations in blood flow are also associated with marked extracellular matrix changes. We concluded that the model was suitable for studying the relative contributions of altering haemodynamic patterns and venous drainage on cerebrovascular changes. We also found that hypertension modulated cerebral vascular changes in addition to disrupted blood flow. [ABSTRACT FROM AUTHOR]

Background: Though administrative databases are increasingly being used for research related to myocardial infarction (MI), the validity of MI diagnoses in these databases has never been synthesized on a large scale. Objective: To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI in administrative data. Methods: MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify MI; or (b) Evaluating the validity of MI codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Results: Thirty studies published from 1984–2010 were included; most assessed codes from the International Classification of Diseases (ICD)-9th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [≥50%] studies was ≥86%, and PPV in most studies was ≥93%. The PPV was higher in the more-recent studies, and lower when criteria that do not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI). Conclusions: Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data if hospitalization data is not available to confirm deaths from MI. [ABSTRACT FROM AUTHOR]

Previous experimental evidence has shown the effect of temperature on the action potential duration (APD). It has also been demonstrated that regional cooling of the heart can prolong the APD and promote the termination of ventricular tachycardia. The aim of this study is to demonstrate the effect of hypothermia in suppressing cardiac arrhythmias using numerical modeling. For this purpose, we developed a mathematical model that couples Pennes’ bioheat equation and the bidomain model to simulate the effect of heat on the cardiac action potential. The simplification of the proposed heat–bidomain model to the heat–monodomain model is provided. A suitable numerical scheme for this coupling, based on a time adaptive mesh finite element method, is also presented. First, we performed two-dimensional numerical simulations to study the effect of heat on a regular electrophysiological wave, with the comparison of the calculated and experimental values of Q10. Then, we demonstrated the effect of global hypothermia in suppressing single and multiple spiral waves. [ABSTRACT FROM AUTHOR]

Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD’s shocks are also very painful and affect a patient’s quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death–Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87–0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power. [ABSTRACT FROM AUTHOR]

Objectives: Healthcare interventions, and particularly those in public health may affect multiple diseases and significantly prolong life. No consensus currently exists for how to estimate comparable healthcare costs across multiple diseases for use in health and public health cost-effectiveness models. We aim to describe a method for estimating comparable disease specific English healthcare costs as well as future healthcare costs from diseases unrelated to those modelled. Methods: We use routine national datasets including programme budgeting data and cost curves from NHS England to estimate annual per person costs for diseases included in the PRIMEtime model as well as age and sex specific costs due to unrelated diseases. Results: The 2013/14 annual cost to NHS England per prevalent case varied between £3,074 for pancreatic cancer and £314 for liver disease. Costs due to unrelated diseases increase with age except for a secondary peak at 30–34 years for women reflecting maternity resource use. Conclusions: The methodology described allows health and public health economic modellers to estimate comparable English healthcare costs for multiple diseases. This facilitates the direct comparison of different health and public health interventions enabling better decision making. [ABSTRACT FROM AUTHOR]

Background: Intellectual conflicts of interest (COI), like financial COI, may threaten the validity and trustworthiness of clinical practice guidelines (CPGs). However, comparatively little is known about intellectual COI in CPGs. This study sought to estimate the prevalence of intellectual COI and corresponding management strategies among cardiology and pulmonology CPGs. Methods: We conducted a retrospective document review of CPGs published by cardiology or pulmonology professional societies from the United States, Canada, or Europe from 2018 to 2019 available via the Emergency Care Research Institute, Guidelines International Network, or Medscape databases. We assessed the percentage of authors with an intellectual COI, defined as i) authorship on a study reviewed by the CPG, ii) authorship of a prior editorial related to a CPG recommendation, or iii) authorship of a prior related CPG. Management strategies assessed included use of GRADE methodology, inclusion of a methodologist, and recusals due to intellectual COI. Outcomes were assessed overall and compared between cardiology and pulmonology CPGs. Results: Among the 39 CPGs identified (14 cardiology, 25 pulmonology), there were a total of 737 authors, of whom 473 (64%) had at least one intellectual COI. Among all CPGs, a median of 67% (Interquartile Range 50%-76%) of authors had at least one intellectual COI, and COI was more prevalent among cardiology compared with pulmonology CPGs (84% vs 57%, p<0.001). There was variable use of management strategies among the CPGs, including use of GRADE methodology (64% of CPGs), inclusion of a methodologist (49%), and recusals due to intellectual COI (0%). Conclusion: Intellectual conflicts of interest appear to be highly prevalent and under-reported among cardiology and pulmonology CPGs, which may threaten their validity. Greater attention to and improved management of intellectual COI by CPG-producing organizations is needed. [ABSTRACT FROM AUTHOR]

Context: The Affordable Care Act resulted in unprecedented reductions in the uninsured population through subsidized private insurance and an expansion of Medicaid. Early estimates from the beginning of 2014 showed that the Medicaid expansion decreased uninsured discharges and increased Medicaid discharges with no change in total discharges. Objective: To provide new estimates of the effect of the ACA on discharges for specific conditions. Design, setting, and participants: We compared outcomes between states that did and did not expand Medicaid using state-level all-capture discharge data from 2009–2014 for 42 states from the Healthcare Costs and Utilization Project’s FastStats database; for a subset of states we used data through 2015. We stratified the analysis by baseline uninsured rates and used difference-in-differences and synthetic control methods to select comparison states with similar baseline characteristics that did not expand Medicaid. Main outcome: Our main outcomes were total and condition-specific hospital discharges per 1,000 population and the share of total discharges by payer. Conditions reported separately in FastStats included maternal, surgical, mental health, injury, and diabetes. Results: The share of uninsured discharges fell in Medicaid expansion states with below (-4.39 percentage points (p.p.), -6.04 –-2.73) or above (-7.66 p.p., -9.07 –-6.24) median baseline uninsured rates. The share of Medicaid discharges increased in both small (6.42 p.p. 4.22–6.62) and large (10.5 p.p., 8.48–12.5) expansion states. Total and most condition-specific discharges per 1,000 residents did not change in Medicaid expansion states with high or low baseline uninsured rates relative to non-expansion states (0.418, p = 0.225), with one exception: diabetes. Discharges for that condition per 1,000 fell in states with high baseline uninsured rates relative to non-expansion states (-0.038 95% p = 0.027). Conclusions: Early changes in payer mix identified in the first two quarters of 2014 continued through the Medicaid expansion’s first year and are distributed across all condition types studied. We found no change in total discharges between Medicaid expansion and non-expansion states, however residents of states that should have been most affected by the Medicaid expansion were less likely to be hospitalized for diabetes. [ABSTRACT FROM AUTHOR]

Background: systemic arterial hypertension is the most prevalent cardiovascular disease; physical activity for hypertensive patients is related to several beneficial cardiovascular adaptations. This paper evaluated the effect of water- and land-ergometry exercise sessions on post-exercise hypotension (PEH) of healthy normotensive subjects versus treated or untreated hypertensive patients. Methods: Forty-five older women composed three experimental groups: normotensive (N, n = 10), treated hypertensive (TH, n = 15) and untreated hypertensive (UH, n = 20). The physical exercise acute session protocol was performed at 75% of maximum oxygen consumption (VO2max) for 45 minutes; systolic (SBP), diastolic (DBP) and mean (MBP) blood pressure were evaluated at rest, peak and at 15, 30, 45, 60, 75 and 90 minutes after exercise cessation. Additionally, the heart rate variability (HRV) was analyzed by R-R intervals in the frequency domain for the assessment of cardiac autonomic function. Results: In both exercise modalities, equivalent increases in SBP were observed from rest to peak exercise for all groups, and during recovery, significant PEH was noted. At 90 minutes after the exercise session, the prevalence of hypotension was significantly higher in water- than in the land-based protocol. Moreover, more pronounced reductions in SBP and DBP were observed in the UH patients compared to TH and N subjects. Finally, exercise in the water was more effective in restoring HRV during recovery, with greater effects in the untreated hypertensive group. Conclusion: Our data demonstrated that water-ergometry exercise was able to induce expressive PEH and improve cardiac autonomic modulation in older normotensive, hypertensive treated or hypertensive untreated subjects when compared to conventional land-ergometry. [ABSTRACT FROM AUTHOR]

Due to a shortage of donor hearts, rotary left ventricular assist devices (LVADs) are used to provide mechanical circulatory support. To address the preload insensitivity of the constant speed controller (CSC) used in conventional LVADs, we developed a preload-based Starling-like controller (SLC). The SLC emulates the Starling law of the heart to maintain mean pump flow () with respect to mean left ventricular end diastolic pressure (PLVEDm) as the feedback signal. The SLC and CSC were compared using a mock circulation loop to assess their capacity to increase cardiac output during mild exercise while avoiding ventricular suction (marked by a negative PLVEDm) and maintaining circulatory stability during blood loss and severe reductions in left ventricular contractility (LVC). The root mean squared hemodynamic deviation (RMSHD) metric was used to assess the clinical acceptability of each controller based on pre-defined hemodynamic limits. We also compared the in-silico results from our previously published paper with our in-vitro outcomes. In the exercise simulation, the SLC increased by 37%, compared to only 17% with the CSC. During blood loss, the SLC maintained a better safety margin against left ventricular suction with PLVEDm of 2.7 mmHg compared to -0.1 mmHg for CSC. A transition to reduced LVC resulted in decreased mean arterial pressure (MAP) and with CSC, whilst the SLC maintained MAP and . The results were associated with a much lower RMSHD value with SLC (70.3%) compared to CSC (225.5%), demonstrating improved capacity of the SLC to compensate for the varying cardiac demand during profound circulatory changes. In-vitro and in-silico results demonstrated similar trends to the simulated changes in patient state however the magnitude of hemodynamic changes were different, thus justifying the progression to in-vitro evaluation. [ABSTRACT FROM AUTHOR]

Background: Chinese populations have a higher proportion of intracerebral hemorrhage (ICH) in total strokes. However, the reasons are not fully understood. Methods: To assess the differences in frequency of major risk factors between ICH and ischemic stroke (IS) in Chinese versus white populations of European descent, we systematically sought studies conducted since 1990 that compared frequency of risk factors between ICH and IS in Chinese or white populations. For each risk factor, in Chinese and Whites separately, we calculated study-specific and random effects pooled prevalence and odds ratios (ORs) for ICH versus IS. Results: Six studies among 36190 Chinese, and seven among 52100 white stroke patients studied hypertension, diabetes, atrial fibrillation (AF), ischemic heart disease (IHD), hypercholesterolemia, smoking and alcohol. Pooled prevalence of AF was significantly lower in Chinese. Pooled ORs for ICH versus IS were mostly similar in Chinese and Whites. However, in Chinese–but not Whites–mean age was lower (62 versus 69 years), while hypertension and alcohol were significantly more frequent in ICH than IS (ORs 1.38, 95% CI 1.18–1.62, and 1.46, 1.12–1.91). Hypercholesterolemia and smoking were significantly less frequent in ICH in Whites, but not Chinese, while IHD, AF and diabetes were less frequent in ICH in both. Conclusions: Different risk factor distributions in ICH and IS raise interesting possibilities about variation in mechanisms underlying the different distributions of pathological types of stroke between Chinese and Whites. Further analyses in large, prospective studies, including adjustment for potential confounders, are needed to consolidate and extend these findings. [ABSTRACT FROM AUTHOR]

This paper presents a phase detection algorithm for four-dimensional (4D) cardiac computed tomography (CT) analysis. The algorithm detects a phase, i.e. a specific three-dimensional (3D) image out of several time-distributed 3D images, with high contrast in the left ventricle and low contrast in the right ventricle. The purpose is to use the automatically detected phase in an existing algorithm that automatically aligns the images along the heart axis. Decision making is based on the contrast agent distribution over time. It was implemented in KardioPerfusion – a software framework currently being developed for 4D CT myocardial perfusion analysis. Agreement of the phase detection algorithm with two reference readers was 97% (95% CI: 82–100%). Mean duration for detection was 0.020 s (95% CI: 0.018–0.022 s), which was times less than the readers needed (s, ). Thus, this algorithm is an accurate and fast tool that can improve work flow of clinical examinations. [ABSTRACT FROM AUTHOR]

Vagal nerve stimulation in cardiac therapy involves delivering electrical current to the vagal sympathetic complex in patients experiencing heart failure. The therapy has shown promise but the mechanisms by which any benefit accrues is not understood. In this paper we model the response to increased levels of stimulation of individual components of the vagal sympathetic complex as a differential activation of each component in the control of heart rate. The model provides insight beyond what is available in the animal experiment in as much as allowing the simultaneous assessment of neuronal activity throughout the cardiac neural axis. The results indicate that there is sensitivity of the neural network to low level subthreshold stimulation. This leads us to propose that the chronic effects of vagal nerve stimulation therapy lie within the indirect pathways that target intrinsic cardiac local circuit neurons because they have the capacity for plasticity. [ABSTRACT FROM AUTHOR]

Objective: Epidemiological studies have shown that E-selectin gene polymorphisms (A561C and C1839T) may be associated with essential hypertension (EH), but the results are conflicting in different ethnic populations. Thus, we performed this meta-analysis to investigate a more authentic association between E-selectin gene polymorphisms and the risk of EH. Methods: We searched the relevant studies for the present meta-analysis from the following electronic databases: PubMed, Embase, Cochrane Library, Google Scholar, Web of Science, Wanfang Data, and China National Knowledge Infrastructure (CNKI). Odds ratios (OR) with 95% confidence interval (CI) were used to evaluate the strength of the association between E-selectin gene polymorphisms and EH susceptibility. The pooled ORs were performed for dominant model, allelic model and recessive model. The publication bias was examined by Begg’s funnel plots and Egger’s test. Results: A total of eleven studies met the inclusion criteria. All studies came from Asians. Ten studies (12 cohorts) evaluated the A561C polymorphism and EH risk, including 2,813 cases and 2,817 controls. The pooled OR was 2.280 (95%CI: 1.893–2.748, P<0.001) in dominant model, 5.284 (95%CI: 2.679–10.420, P<0.001) in recessive model and 2.359 (95%CI: 1.981–2.808, P = 0.001) in allelic model. Four studies (six cohorts) evaluated C1839T polymorphism and EH risk, including 1,700 cases and 1,681 controls. The pooled OR was 0.785 (95%CI: 0.627–0.983, P = 0.035) in dominant model, 1.250 (95%CI: 0.336–4.652, P = 0.739) in recessive model and 0.805 (95%CI: 0.649–0.999, P = 0.049) in allelic model. Conclusion: The current meta-analysis concludes that the C allele of E-selectin A561C gene polymorphism might increase the EH risk in Asian population, whereas the T allele of E-selectin C1839T gene polymorphism might decrease the EH risk. [ABSTRACT FROM AUTHOR]

Background: Rheumatic Heart Disease (RHD) remains one of the major causes of death and disability in developing countries. This preventable, treatable but not curable form of cardiovascular disease is needlessly killing scores of children and youth mainly due to the misunderstanding of the burden of the disease in these countries. We sought to describe the prevalence of RHD at one of the major referral cardiology clinics in Ethiopia. Methods: This was a retrospective cross-sectional chart review of all patients referred for a cardiopathy at the Tikur Anbessa Referral Cardiac Clinic from June 2015 to August 2018. We excluded records of patients with a non-cardiac diagnosis and those without a clear diagnosis. A predesigned and tested EXCEL form was used to collect the data. The data was encoded directly from the patient record files. MATLAB's statistics toolbox (MATLAB2019b) was used for statistical analysis. Results: Among the total 7576 records analyzed 59.5% of the patients were women. 83.1% of the data belonged to adult patients with the largest concentration reported in the 18 to 27 age group. 69.7% of the patients were from urban areas. The median age of the study population was 30 (interquartile range = 21–50). 4151 cases were caused by RHD which showed that RHD constituted 54.8% of the cases. The median age for RHD patients was 25 (interquartile range = 19–34). The second most prevalent disease was hypertensive heart disease which constituted 13.6% that was followed by congenital heart disease with 9% prevalence rate. Conclusion: The results of this study indicated the extent of the RHD prevalence in Ethiopia's cardiac hospital was 54.8%. What was more critical was that almost 70% of the RHD patients were mainly the working-age group(19 to 34 years). [ABSTRACT FROM AUTHOR]

Background: There has been an increase in the number of individuals aged ≥60 years in Tanzania and in sub Saharan Africa in general due to improved survival. However, data is scarce on the disease burden and outcomes following admission in this population. We herein describe the pattern of diagnoses, outcomes and factors associated with the outcomes among elderly patients admitted at Muhimbili National Hospital (MNH) and Jakaya Kikwete Cardiac Institute (JKCI) medical wards. Methodology: From October to December 2017, we consecutively enrolled patients aged ≥60 years (elderly) admitted to the MNH and JKCI medical wards. The ICD 10 was used to code for disease diagnosis at discharge or death. The Modified Barthel index was used to assess for functional activity on admission and at discharge. Results: We enrolled 336 (30.1%) elderly participants out of 1301 medical admissions. The mean age ± SD was 70.6 ± 8.9 years; 169 (50%) were female and the average number of diagnoses was 2 per participant. The most common diagnoses were: hypertension 151 (44.9%), stroke 106 (31.5%), heart failure 62 (18.5%), pneumonia 60 (17.9%), diabetes mellitus 58 (17.3%) and chronic kidney disease 55 (16.4%). The median duration of hospital stay was 5 (IQR 3–10) days and in-hospital mortality was 86 (25.6%), 56 (65%) deaths were due to non-communicable diseases and 48 (55.8%) deaths occurred within 72 hours of hospitalization. A modified Barthel score ≤20 on admission was associated with an OR 15.43 (95% CI: 7.5–31.7, p<0.001) for death. Conclusion: Elderly patients constituted a significant proportion of medical admissions at MNH and JKCI with high in-hospital mortality. A modified Barthel index score ≤20 during admission is associated with mortality and can be used to identify patients requiring special attention. [ABSTRACT FROM AUTHOR]