55 results match your criteria: "Biomedical and Translational Informatics Institute[Affiliation]"
Burden of Multiple Chronic Conditions among Patients with Urological Cancer.
J Urol
February 2018
Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania.
Purpose: We describe age, multiple chronic condition profiles and health system contact in patients with urological cancer.
Materials And Methods: Using Geisinger Health System electronic health records we identified adult primary care patients and a subset with at least 1 urology encounter between 2001 and 2015. The Agency for Health Care Research and Quality Chronic Condition Indicator and Clinical Classifications Software tools were applied to ICD-9 codes to identify chronic conditions.
Impaired right ventricular contractile function in childhood obesity and its association with right and left ventricular changes: a cine DENSE cardiac magnetic resonance study.
J Cardiovasc Magn Reson
June 2017
Department of Imaging Science and Innovation, Geisinger Health System, 100 North Academy Avenue, Danville, 17822-4400, PA, USA.
Article Synopsis
- Pediatric obesity is a rising public health issue linked to serious health risks like heart disease and early death, highlighting the need to explore right ventricular (RV) changes in addition to left ventricular (LV) issues in obese children.
- A study involving 103 children aged 8-18 used advanced imaging techniques to assess the geometry and function of the RV, discovering significant differences between healthy-weight and obese/overweight groups.
- Results showed that obese/overweight children had a 22% increase in RV mass and poorer RV function (longitudinal strain), with some exhibiting more severe conditions like LV concentric hypertrophy, which further impaired RV functionality.
Electronic health record phenotype in subjects with genetic variants associated with arrhythmogenic right ventricular cardiomyopathy: a study of 30,716 subjects with exome sequencing.
Genet Med
November 2017
Department of Imaging Science and Innovation, Geisinger Health System, Danville, Pennsylvania, USA.
Article Synopsis
- ARVC is a genetic heart disease, and researchers aimed to assess the prevalence of related findings in genes associated with it through exome sequencing of over 30,000 individuals.
- The study found that subjects with pathogenic loss-of-function (pLOF) variants and variants of uncertain significance (VUS) did not have a formal diagnosis of ARVC in their electronic health records (EHR).
- Overall, the results indicate that pLOF variants and VUS were not linked to ARVC-related health issues among the cohort, raising questions about the reliability of EHR reviews in predicting ARVC.
Novel Screening Tool for Stroke Using Artificial Neural Network.
Stroke
June 2017
From the Biocomplexity Institute (V.A., R.Z.), Department of Industrial and Systems Engineering (G.T.), and Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute (R.H., J.B.-R.), Virginia Tech, Blacksburg; Biomedical and Translational Informatics Institute (V.A.) and Department of Neurology (R.Z.), Geisinger Health System, Danville, PA; Department of Neurology, University of Tennessee Health Science Center, Memphis (N.G., G.T., L.E., J.E.M., A.W.A., A.V.A., R.Z.); Second Department of Neurology, "Attikon University Hospital," School of Medicine, University of Athens, Greece (N.H.); and Neurovascular Imaging Research Core and UCLA Stroke Center, University of California, Los Angeles (D.S.L.).
Background And Purpose: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting.
Methods: Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.
Using a respiratory navigator significantly reduces variability when quantifying left ventricular torsion with cardiovascular magnetic resonance.
J Cardiovasc Magn Reson
March 2017
Department of Pediatrics, University of Kentucky, Lexington, KY, USA.
Background: Left ventricular (LV) torsion is an important indicator of cardiac function that is limited by high inter-test variability (50% of the mean value). We hypothesized that this high inter-test variability is partly due to inconsistent breath-hold positions during serial image acquisitions, which could be significantly improved by using a respiratory navigator for cardiovascular magnetic resonance (CMR) based quantification of LV torsion.
Methods: We assessed respiratory-related variability in measured LV torsion with two distinct experimental protocols.