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Background: Coronary artery disease (CAD) in patients with severe aortic stenosis (AS) is common and may be associated with worse outcomes. Computed tomography coronary angiography (CTCA) and fractional flow reserve derived from computed tomography (FFRCT) are tools for comprehensive coronary assessment. The utility and safety of CTCA and FFRCT in the work-up for transcatheter aortic valve replacement (TAVR) is not established, especially in an evolving landscape that involves younger TAVR patients. The FUTURE-AS Registry will assess the utility and safety of cardiac-optimized CTCA and FFRCT to evaluate CAD and guide referral for downstream invasive coronary angiography (ICA) in patients with severe AS being considered for TAVR. Methods: FUTURE-AS is an international, prospective, multicenter registry of patients with severe AS referred for TAVR being assessed for CAD with CTCA and FFRCT. The primary end point is the per-patient sensitivity and negative predictive value of CTCA and FFRCT for identifying anatomical and physiologically significant CAD compared to ICA and invasive FFR. The safety end point is the incidence of symptomatic hypotension or bradycardia requiring intervention following the administration of nitroglycerin or ß-blocker medications. Feasibility end points include the incidence of noninterpretable CTCA scans and CTCA scans not adequate for FFR analysis. Other utility end points include specificity, positive predictive value, and accuracy of CTCA and FFRCT. Lastly, the potential of a CTCA and FFRCT guided strategy to defer pre-TAVR ICA will be assessed. Conclusions: FUTURE-AS will characterize the utility, safety, and feasibility of CTCA and FFRCT for coronary assessment pre-TAVR.
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BACKGROUND: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. OBJECTIVE: This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. METHODS: Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. RESULTS: A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. CONCLUSIONS: Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. TRIAL REGISTRATION: ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cvdhj.2021.07.002.
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With the rise in maternal mortality rates and the growing body of epidemiological evidence linking pregnancy history to maternal cardiovascular health, it is essential to comprehend the vascular remodeling that occurs during gestation. The maternal body undergoes significant hemodynamic alterations which are believed to induce structural remodeling of the cardiovascular system. Yet, the effects of pregnancy on vascular structure and function have not been fully elucidated. Such a knowledge gap has limited our understanding of the etiology of pregnancy-induced cardiovascular disease. Towards bridging this gap, we measured the biaxial mechanical response of the murine descending thoracic aorta during a normotensive late-gestation pregnancy. Non-invasive hemodynamic measurements confirmed a 50% increase in cardiac output in the pregnant group, with no changes in peripheral blood pressure. Pregnancy was associated with significant wall thickening ( â¼14%), an increase in luminal diameter ( â¼6%), and material softening in both circumferential and axial directions. This expansive remodeling of the tissue resulted in a reduction in tensile wall stress and intrinsic tissue stiffness. Collectively, our data indicate that an increase in the geometry of the vessel may occur to accommodate for the increase in cardiac output and blood flow that occurs in pregnancy. Similarly, wall thickening accompanied by increased luminal diameter, without a change in blood pressure may be a necessary mechanism to decrease the tensile wall stress, and avoid pathophysiological events following late gestation.
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The widespread use of cardiac computed tomography and cardiac magnetic resonance imaging in patients undergoing echocardiography presents an opportunity to correlate the images side by side. Accordingly, the aim of this report is to review aspects of the standard echocardiographic examination alongside similarly oriented images from the two tomographic imaging modalities. It is hoped that this exercise will enhance understanding of the structures depicted by echocardiography as they relate to other structures in the thorax. In addition to reviewing basic cardiac anatomy, the authors take advantage of these correlations with computed tomography and cardiac magnetic resonance imaging to better understand the issue of foreshortening, a common pitfall in transthoracic echocardiography. The authors also highlight an important role that three-dimensional echocardiography can potentially play in the future, especially as advances in image processing permit higher fidelity multiplanar reconstruction images.
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Ecocardiografía Tridimensional , Ecocardiografía , Humanos , Ecocardiografía/métodos , Atrios Cardíacos/diagnóstico por imagen , Ventrículos Cardíacos/diagnóstico por imagen , Ecocardiografía Tridimensional/métodos , Pericardio/diagnóstico por imagenRESUMEN
Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.
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Klippel-Trénaunay syndrome (KTS) is a rare and complex congenital syndrome defined as the triad of cutaneous capillary malformation, bone and soft tissue hypertrophy, and venous and lymphatic malformations. KTS is thought to be due to a somatic mutation in phosphatidyl-inositol 3 kinase. It belongs to a group of syndromes termed the PI3CA-Related Overgrowth Spectrum (PROS) disorders. Because of the rarity and clinical heterogeneity of these disorders, management is patient specific, and best evidence guidelines are lacking. The most common clinical complications are thromboembolism, thrombophlebitis, pain, bleeding, and high-output heart failure. Surgery is recommended for hemangiomas and chronic venous insufficiency. The early identification of children with PROS disorders has allowed treatment with mTOR inhibitors which have been shown to be effective. The recent development of a direct PI3K inhibitor (alpelisib) has shown promise in preventing abnormal growth and long-term complications of KTS. This report documents a case of high-output heart failure due to the vascular malformations associated with KTS in a 57-year-old male patient and discusses current literature regarding the management of KTS with inhibitors of mTOR and PI3KCA.
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Adaptive thermogenesis by brown adipose tissue (BAT) dissipates calories as heat, making it an attractive anti-obesity target. Yet how BAT contributes to circulating metabolite exchange remains unclear. Here, we quantified metabolite exchange in BAT and skeletal muscle by arteriovenous metabolomics during cold exposure in fed male mice. This identified unexpected metabolites consumed, released and shared between organs. Quantitative analysis of tissue fluxes showed that glucose and lactate provide ~85% of carbon for adaptive thermogenesis and that cold and CL316,243 trigger markedly divergent fuel utilization profiles. In cold adaptation, BAT also dramatically increases nitrogen uptake by net consuming amino acids, except glutamine. Isotope tracing and functional studies suggest glutamine catabolism concurrent with synthesis via glutamine synthetase, which avoids ammonia buildup and boosts fuel oxidation. These data underscore the ability of BAT to function as a glucose and amino acid sink and provide a quantitative and comprehensive landscape of BAT fuel utilization to guide translational studies.
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Tejido Adiposo Pardo , Glutamina , Masculino , Animales , Ratones , Tejido Adiposo Pardo/metabolismo , Glutamina/metabolismo , Glucosa/metabolismo , Termogénesis/fisiología , Músculo Esquelético/metabolismoRESUMEN
Epicardial adipose tissue (EAT) is an endocrine organ containing a host of cell types and undoubtedly serving a multitude of important physiologic functions. Aging and obesity cause hypertrophy of EAT. There is great interest in the possible connection between EAT and cardiovascular disease, in particular, atrial fibrillation (AF). Increased EAT is independently associated with AF and adverse events after AF ablation (e.g., recurrence of AF, and stroke). In general, the amount of EAT correlates with BMI or visceral adiposity. Yet on a molecular level, there are similarities and differences between epicardial and abdominal visceral adipocytes. In comparison to subcutaneous adipose tissue, both depots are enriched in inflammatory cells and chemokines, even in normal conditions. On the other hand, in comparison to visceral fat, epicardial adipocytes have an increased rate of fatty acid release, decreased size, and increased vascularity. Several studies have described an association between fibrosis of EAT and fibrosis of the underlying atrial myocardium. Others have discovered paracrine factors released from EAT that could possibly mediate this association. In addition to the adjacent atrial cardiomyocytes, EAT contains a robust stromal-vascular fraction and surrounds the ganglionic plexi of the cardiac autonomic nervous system (cANS). The importance of the cANS in the pathogenesis of atrial fibrillation is well known, and it is quite likely that there is feedback between EAT and the cANS. This complex interplay may be crucial to the maintenance of normal sinus rhythm or the development of atrial fibrillation. The extent the adipocyte is a microcosm of metabolic health in the individual patient may determine which is the predominant rhythm.
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Fibrilación Atrial , Humanos , Atrios Cardíacos , Obesidad/metabolismo , Fibrosis , Adipocitos/metabolismoRESUMEN
OBJECTIVE: With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. METHODS: We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. CONCLUSION: A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. SIGNIFICANCE: As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.
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Fibrilación Atrial , Dispositivos Electrónicos Vestibles , Algoritmos , Artefactos , Electrocardiografía/métodos , Frecuencia Cardíaca , Humanos , Fotopletismografía/métodos , Procesamiento de Señales Asistido por ComputadorRESUMEN
OBJECTIVE: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. METHODS: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung's Gear S3 and Galaxy Watch 3. RESULTS: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors-30% and 66% lower-and mean heart rate and mean interbeat interval estimation errors-60% and 77% lower-when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. CONCLUSION: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. SIGNIFICANCE: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
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Fibrilación Atrial , Complejos Prematuros Ventriculares , Algoritmos , Fibrilación Atrial/diagnóstico , Electrocardiografía , Frecuencia Cardíaca/fisiología , Humanos , Fotopletismografía/métodos , Complejos Prematuros Ventriculares/diagnósticoRESUMEN
Aortic diseases requiring surgery in childhood are distinctive and rare. Very few reports in the literature account for the occurrence of multiple thoracic aortic aneurysms in the same pediatric patient because of a genetic cause. We report a rare occurrence of severe thoracic aortic aneurysms (involving the ascending, arch and descending aortic segments) with severe aortic insufficiency in a 7-year-old female child secondary to the extremely rare and often lethal genetic disorder, cutis laxa. She was eventually identified as a carrier of a homozygous EFEMP2 (alias FBLN4) mutation. This gene encodes the extracellular matrix protein fibulin-4, and its mutation is associated with autosomal recessive cutis laxa type 1B that leads to severe aortopathy with aneurysm formation and vascular tortuosity. Parents of the child were not known to be consanguineous. Significant symptomatic improvement in the patient could be discerned after timely intervention with the valve-sparing aortic root replacement (David V procedure) and a concomitant aortic arch replacement. This is a unique report with a successful outcome that highlights the occurrence of a rare hereditary aortopathy associated with a high morbidity and mortality, and the importance of an early diagnosis and timely management. It also offers insight to physicians in having a very broad differential and multimodal approach in handling rare pediatric cardio-pathologies with a genetic predisposition.
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Background: Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods: In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.
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Background: Atrial fibrillation (AF) is a common heart rhythm disorder that elevates stroke risk. Stroke survivors undergo routine heart rhythm monitoring for AF. Smartwatches are capable of AF detection and potentially can replace traditional cardiac monitoring in stroke patients. Objective: The goal of Pulsewatch is to assess the accuracy, usability, and adherence of a smartwatch-based AF detection system in stroke patients. Methods: The study will consist of two parts. Part I will have 6 focus groups with stroke patients, caretakers, and physicians, and a Hack-a-thon, to inform development of the Pulsewatch system. Part II is a randomized clinical trial with 2 phases designed to assess the accuracy and usability in the first phase (14 days) and adherence in the second phase (30 days). Participants will be randomized in a 3:1 ratio (intervention to control) for the first phase, and both arms will receive gold-standard electrocardiographic (ECG) monitoring. The intervention group additionally will receive a smartphone/smartwatch dyad with the Pulsewatch applications. Upon completion of 14 days, participants will be re-randomized in a 1:1 ratio. The intervention group will receive the Pulsewatch system and a handheld ECG device, while the control group will be passively monitored. Participants will complete questionnaires at enrollment and at 14- and 44-day follow-up visits to assess various psychosocial measures and health behaviors. Results: Part I was completed in August 2019. Enrollment for Part II began September 2019, with expected completion by the end of 2021. Conclusion: Pulsewatch aims to demonstrate that a smartwatch can be accurate for real-time AF detection, and that older stroke patients will find the system usable and will adhere to monitoring.
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OBJECTIVE: Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a novel density Poincaré plot-based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. METHODS: First, we propose the generation of this new density Poincaré plot which is derived from the difference of the heart rate (DHR) and provides the overlapping phase-space trajectory information of the DHR. Next, from this density Poincaré plot, several image processing domain-based approaches including statistical central moments, template correlation, Zernike moment, discrete wavelet transform and Hough transform features are used to extract suitable features. Subsequently, the infinite latent feature selection algorithm is implemented to rank the features. Finally, classification of AF vs. PAC/PVC is performed using K-Nearest Neighbor, Support vector machine (SVM) and Random Forest (RF) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 10 AF and 10 PAC/PVC subjects. Results- During the segment-wise 10-fold cross-validation, SVM achieved the best performance with 98.99% sensitivity, 95.18% specificity and 97.45% accuracy with the extracted features. In subject-wise scenario, RF achieved the highest accuracy of 91.93%. Moreover, we further validated the proposed method using two other databases: wearable armband ECG data and the Physionet AFPDB. 100% PAC detection accuracy was obtained for both databases without any further training. CONCLUSION: Our proposed density Poincaré plot-based method showed superior performance when compared with four existing algorithms; thus showing the efficacy of the extracted image domain-based features. SIGNIFICANCE: From intensive care unit's ECG to wearable armband ECGs, the proposed method is shown to discriminate PAC/PVCs from AF with high accuracy.
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Fibrilación Atrial , Complejos Atriales Prematuros , Complejos Prematuros Ventriculares , Algoritmos , Fibrilación Atrial/diagnóstico , Complejos Atriales Prematuros/diagnóstico , Electrocardiografía , Atrios Cardíacos , Humanos , Aprendizaje Automático , Procesamiento de Señales Asistido por Computador , Complejos Prematuros Ventriculares/diagnósticoRESUMEN
The myeloid cells infiltrating the heart early after acute myocardial infarction elaborate a secretome that largely orchestrates subsequent ventricular wall repair. Regulating this innate immune response could be a means to improve infarct healing. To pilot this concept, we utilized (ß1,3-d-) glucan-encapsulated small interfering RNA (siRNA)-containing particles (GeRPs), targeting mononuclear phagocytes, delivered to mice as a one-time intramyocardial injection immediately after acute infarction. Findings demonstrated that cardiac macrophages phagocytosed GeRPs in vivo and had little systemic dissemination, thus providing a means to deliver local therapeutics. Acute infarcts were then injected in vivo with phosphate-buffered saline (PBS; vehicle) or GeRPs loaded with siRNA to Map4k4, and excised hearts were examined at 3 and 7 days by quantitative polymerase chain reaction, flow cytometry, and histology. Compared with infarcted PBS-treated hearts, hearts with intrainfarct injections of siRNA-loaded GeRPs exhibited 69-89% reductions in transcripts for Map4k4 (mitogen-activated protein kinase kinase kinase kinase 4), interleukin (IL)-1ß, and tumor necrosis factor α at 3 days. Expression of other factors relevant to matrix remodeling-monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinases, hyaluronan synthases, matricellular proteins, and profibrotic factors transforming growth factor beta (TGF-ß), and connective tissue growth factor (CTGF)-were also decreased. Most effects peaked at 3 days, but, in some instances (Map4k4, IL-1ß, TGF-ß, CTGF, versican, and periostin), suppression persisted to 7 days. Thus, direct intramyocardial GeRP injection could serve as a novel and clinically translatable platform for in vivo RNA delivery to intracardiac macrophages for local and selective immunomodulation of the infarct microenvironment.