Search Results - "Clopton, P."

A Behavioral Task Predicts Conversion to Mild Cognitive Impairment and Alzheimer’s Disease by Zola, Stuart M., Manzanares, C. M., Clopton, P., Lah, J. J., Levey, A. I.

“…Background/Rationale: Currently, we cannot reliably differentiate individuals at risk of cognitive decline, for example, mild cognitive impairment (MCI),…”
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A rapid test for B-type natriuretic peptide correlates with falling wedge pressures in patients treated for decompensated heart failure: A pilot study by Kazanegra, Radmila, Cheng, Van, Garcia, Alex, Krishnaswamy, Padma, Gardetto, Nancy, Clopton, Paul, Maisel, Alan

“…Objectives: To determine if changes in B-type natriuretic peptide (BNP) levels can accurately reflect acute changes in pulmonary capillary wedge pressure…”
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Diagnostic ability of B-type natriuretic peptide and impedance cardiography: Testing to identify left ventricular dysfunction in hypertensive patients by Bhalla, Vikas, Isakson, Susan, Bhalla, Meenakshi A., Lin, Jeannette P., Clopton, Paul, Gardetto, Nancy, Maisel, Alan S.

“…Patients with hypertension are at high risk for the development of left ventricular dysfunction (LVD). Echocardiography is considered to be the gold standard…”
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WY14,643, a PPARα ligand, attenuates expression of anti-glomerular basement membrane disease by Archer, D.C, Frkanec, J.T, Cromwell, J, Clopton, P, Cunard, R

“…Peroxisome proliferator-activated receptor alpha (PPARα) ligands are medications used to treat hyperlipidaemia and atherosclerosis. Increasing evidence…”
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Sleep apnea independently predicts incident atrial fibrillation in the young - Implications for targeted screening by Deb, B, Vasireddi, S K, Bhatia, N K, Rogers, A J, Clopton, P, Narayan, S M

“…Abstract Background The risk factors predisposing to AF and ischemic stroke are not well defined in the younger population. That said, routine screening for…”
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232: The Audicor S3: Use in the BNP Indeterminate Zone and Impact on Physician Clinical Confidence in Acute Heart Failure by Collins, S.P, Peacock, W.F, Clopton, P, Kontos, M.C, Mueller, C, Diercks, D.B, Hiestand, B, Hogan, C.J, Nowak, R.M, Maisel, A.S

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152: Audicor S3 Outperforms Emergency Physicians’ Ability to Diagnose Acute Heart Failure at the Bedside by Collins, S.P, Peacock, W.F, Clopton, P, Kontos, M.C, Mueller, C, Diercks, D.B, Hiestand, B, Hogan, C.J, Nowak, R.M, Maisel, A.S

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Efficacy and Safety of the Farnesoid X Receptor Agonist Obeticholic Acid in Patients With Type 2 Diabetes and Nonalcoholic Fatty Liver Disease by Mudaliar, Sunder, Henry, Robert R, Sanyal, Arun J, Morrow, Linda, Marschall, Hanns–Ulrich, Kipnes, Mark, Adorini, Luciano, Sciacca, Cathi I, Clopton, Paul, Castelloe, Erin, Dillon, Paul, Pruzanski, Mark, Shapiro, David

“…Background & Aims Obeticholic acid (OCA; INT-747, 6α-ethyl-chenodeoxycholic acid) is a semisynthetic derivative of the primary human bile acid chenodeoxycholic…”
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Novel scoring system for predicting mortality in patients with ventricular arrhythmias: Analysis from a 24,000 patient cohort by Deb, B, Rogers, A J, Tjong, F V Y, Somani, S, Desai, Y, Azizi, Z, Chang, H J, Bhatia, N K, Clopton, P, Narayan, S M

“…Abstract Background Several scoring systems have been developed to predict mortality in ventricular tachycardias (VT) in selected populations (for example,…”
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Predicting success of atrial fibrillation ablation: comparing machine learning approaches of intracardiac electrograms by Feng, R, Ganesan, P, Deb, B, Ruiperez-Campillo, S, Chang, H J, Clopton, P, Rogers, A J, Rodrigo, M, Tjong, F V Y, Zaharia, M, Narayan, S M

“…Abstract Introduction Machine learning (ML) and Deep learning (DL) neural networks have been used to predict which patients will respond to atrial fibrillation…”
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Imaging of osseous and cartilaginous intraarticular bodies in the knee: comparison of MR imaging and MR arthrography with CT and CT arthrography in cadavers by Brossmann, J, Preidler, K W, Daenen, B, Pedowitz, R A, Andresen, R, Clopton, P, Trudell, D, Pathria, M, Resnick, D

“…To compare magnetic resonance (MR) imaging and MR arthrography with computed tomography (CT) and CT arthrography in the detection of intraarticular bodies in…”
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Artificial intelligence to reduce artifact in cardiac electrophysiological signals by Ruiperez-Campillo, S, Deb, B, Feng, R, Ganesan, P, Tjong, F V Y, Clopton, P, Rogers, A J, Narayan, S M

“…Abstract Background/Introduction Signals in Electrophysiology cases are often noisy despite laboratory shielding and filtering, and current noise-reduction…”
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Automatic left atrial segmentation from cardiac CT using computer graphics imaging and deep learning by Feng, R, Deb, B, Ganesan, P, Rogers, A J, Ruiperez-Campillo, S, Clopton, P, Tjong, F V, Chang, H J, Rodrigo, M, Zaharia, M, Narayan, S M

“…Abstract Introduction Segmenting left atrial (LA) substructures, including the LA body, appendage (LAA), and pulmonary veins (PVs), from computed tomography…”
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Novel electrogram featurization reveals a spectrum of response to ablation from atrial tachycardia to types of atrial fibrillation by Ganesan, P, Rogers, A J, Deb, B, Feng, R, Ruiperez-Campillo, S, Tjong, F V, Bhatia, N, Clopton, P, Rappel, W J, Narayan, S M

“…Abstract Background Although atrial tachycardia (AT) may interconvert with fibrillation (AF) in many patients, it is undefined if this represents a…”
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Spatiotemporal signatures of response to atrial fibrillation ablation by Ganesan, P, Rogers, A J, Deb, B, Feng, R, Rodrigo, M, Ruiperez-Campillo, S, Tjong, F V, Bhatia, N, Clopton, P, Rappel, W J, Narayan, S M

“…Abstract Background Atrial fibrillation (AF) can have organized regions, in the form of consistent dominant frequency sites, focal or reentrant sites, but it…”
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Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning by Ruiperez-Campillo, S, Deb, B, Feng, R, Ganesan, P, Tjong, F V Y, Clopton, P, Rogers, A J, Narayan, S M

“…Abstract Background/Introduction Mapping AF is challenging. Monophasic action potentials (MAPs) show that most of the recorded signals are not representing the…”
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Noise reduction in electrophysiological signals using transfer machine learning by Ruiperez-Campillo, S, Deb, B, Feng, R, Ganesan, P, Clopton, P, Rogers, AJ, Narayan, SM

“…Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background/Introduction Reducing…”
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Defining refractoriness in single atrial beats using autoencoder neural networks by Ruiperez-Campillo, S, Deb, B, Feng, R, Ganesan, P, Clopton, P, Rogers, AJ, Narayan, SM

“…Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background Mapping atrial…”
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Machine-learned physiological signatures from the ECG predict sudden death in ischemic cardiomyopathy by Deb, B, Selvalingam, A, Alhusseini, M, Rogers, A, Ganesan, P, Feng, R, Clopton, P, Ruiperez-Campillo, S, Narayan, S

“…Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH)…”
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Machine learned clusters explain heterogeneity in outcomes from map-guided ablation of Atrial Fibrillation results from the large PROspective STanford AF Registry (ProSTAR) by Deb, B, Rogers, A J, Bhatia, N K, Baykaner, T, Turakhia, M, Clopton, P L, Chang, H J, Brodt, C, Narayan, S M, Wang, P J, Viswanathan, M N

“…Abstract Background Several mapping systems are being introduced to guide atrial fibrillation (AF) ablation to patient-specific regions of interest. However,…”
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