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Development and validation of a model predicting mild stroke severity on admission using electronic health record data
Initial stroke severity is a potent modifier of stroke outcomes but this information is difficult to obtain from electronic health record (EHR) data. This limits the ability to risk-adjust for evaluations of stroke care and outcomes at a population level. The purpose of this analysis was to develop and validate a predictive model of initial stroke severity using EHR data elements.
Source: Journal of Stroke and Cerebrovascular Diseases - July 18, 2023 Category: Neurology Authors: Kimberly J. Waddell, Laura J. Myers, Anthony J. Perkins, Jason J. Sico, Ali Sexson, Laura Burrone, Stanley Taylor, Brian Koo, Joanne K. Daggy, Dawn M. Bravata Source Type: research

Sex and age effects on risk of non-traumatic subarachnoid hemorrhage: Retrospective cohort study of 124,234 cases using electronic health records
This study describes the antecedent characteristics of SAH patients, compares the risk of SAH between women and men, and explores if this changes with age.
Source: Journal of Stroke and Cerebrovascular Diseases - May 23, 2023 Category: Neurology Authors: Charlotte H Harrison, Maxime Taquet, Paul J Harrison, Peter J Watkinson, Matthew J Rowland Source Type: research

Integration of Real-Time Electronic Health Records and Wireless Technology in a Mobile Stroke Unit
Background: UCHealth's Mobile Stroke Unit (MSU) at University of Colorado Hospital is an ambulance equipped with a computed tomography (CT) scanner and tele-stroke capabilities that began clinical operation in Aurora, Colorado January 2016. As one of the first MSU's in the United States, it was necessary to design unique and dynamic information technology infrastructure. This includes high-speed cellular connectivity, Health Insurance Portability and Accountability Act compliance, cloud-based and remote access to electronic medical records (EMR), and reliable and rapid image transfer.
Source: Journal of Stroke and Cerebrovascular Diseases - July 11, 2019 Category: Neurology Authors: Brandi Schimpf, Kathy Deanda, David A. Severenuk, Tara M. Montgomery, Gregory D. Cooley, Robert G. Kowalski, Daniel Vela-Duarte, William J. Jones Source Type: research

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing
This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping. Methods: Among IS patients from an observational registry with TOAST subtyping adjudicated by board-certified vascular neurologists, we analyzed unstructured text-based EHR data including neurology progress notes and neuroradiology reports using natural language processing.
Source: Journal of Stroke and Cerebrovascular Diseases - May 14, 2019 Category: Neurology Authors: Ravi Garg, Elissa Oh, Andrew Naidech, Konrad Kording, Shyam Prabhakaran Source Type: research

The Kentucky Appalachian Stroke Registry (KApSR)
The population of rural Kentucky and West Virginia has a disproportionately high incidence of stroke and stroke risk factors. The Kentucky Appalachian Stroke Registry (KApSR) is a novel registry of stroke patients developed to collect demographic and clinical data in real time from these patients' electronic health records.
Source: Journal of Stroke and Cerebrovascular Diseases - December 18, 2017 Category: Neurology Authors: Patrick Kitzman, Marc Wolfe, Kelley Elkins, Justin F. Fraser, Stephen L. Grupke, Michael R. Dobbs Source Type: research

Using Radiological Data to Estimate Ischemic Stroke Severity
Risk-adjusted poststroke mortality has been proposed for use as a measure of stroke care quality. Although valid measures of stroke severity (e.g., the National Institutes of Health Stroke Scale [NIHSS]) are not typically available in administrative datasets, radiology reports are often available within electronic health records. We sought to examine whether admission head computed tomography data could be used to estimate stroke severity.
Source: Journal of Stroke and Cerebrovascular Diseases - January 13, 2016 Category: Neurology Authors: Jason J. Sico, Michael S. Phipps, John Concato, Cynthia Brandt, Carolyn K. Wells, Albert C. Lo, Stephen E. Nadeau, Linda S. Williams, Mark Gorman, John L. Boice, Dawn M. Bravata Source Type: research