Syed F Ali1, Gordian J Hubert1, Jeffrey A Switzer1, Jennifer J Majersik1, Roland Backhaus1, L Wylie Shepard1, Kishore Vedala1, Lee H Schwamm2. 1. From the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston (S.F.A., L.H.S.); Department of Neurology and Neurological Intensive Care, Staedtisches Klinikum München, TeleMedical Project for integrative Stroke Care, Munich, Germany (G.J.H.); Department of Neurology, Augusta University, GA (J.S., K.V.); Division of Vascular Neurology, University of Utah, Salt Lake City (J.J.M., L.W.S.); and Department of Neurology, University Regensburg, Germany (R.B.). 2. From the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston (S.F.A., L.H.S.); Department of Neurology and Neurological Intensive Care, Staedtisches Klinikum München, TeleMedical Project for integrative Stroke Care, Munich, Germany (G.J.H.); Department of Neurology, Augusta University, GA (J.S., K.V.); Division of Vascular Neurology, University of Utah, Salt Lake City (J.J.M., L.W.S.); and Department of Neurology, University Regensburg, Germany (R.B.). lschwamm@partners.org.
Abstract
BACKGROUND AND PURPOSE: Up to 30% of acute stroke evaluations are deemed stroke mimics, and these are common in telestroke as well. We recently published a risk prediction score for use during telestroke encounters to differentiate stroke mimics from ischemic cerebrovascular disease derived and validated in the Partners TeleStroke Network. Using data from 3 distinct US and European telestroke networks, we sought to externally validate the TeleStroke Mimic (TM) score in a broader population. METHODS: We evaluated the TM score in 1930 telestroke consults from the University of Utah, Georgia Regents University, and the German TeleMedical Project for Integrative Stroke Care Network. We report the area under the curve in receiver-operating characteristic curve analysis with 95% confidence interval for our previously derived TM score in which lower TM scores correspond with a higher likelihood of being a stroke mimic. RESULTS: Based on final diagnosis at the end of the telestroke consultation, there were 630 of 1930 (32.6%) stroke mimics in the external validation cohort. All 6 variables included in the score were significantly different between patients with ischemic cerebrovascular disease versus stroke mimics. The TM score performed well (area under curve, 0.72; 95% confidence interval, 0.70-0.73; P<0.001), similar to our prior external validation in the Partners National Telestroke Network. CONCLUSIONS: The TM score's ability to predict the presence of a stroke mimic during telestroke consultation in these diverse cohorts was similar to its performance in our original cohort. Predictive decision-support tools like the TM score may help highlight key clinical differences between mimics and patients with stroke during complex, time-critical telestroke evaluations.
BACKGROUND AND PURPOSE: Up to 30% of acute stroke evaluations are deemed stroke mimics, and these are common in telestroke as well. We recently published a risk prediction score for use during telestroke encounters to differentiate stroke mimics from ischemic cerebrovascular disease derived and validated in the Partners TeleStroke Network. Using data from 3 distinct US and European telestroke networks, we sought to externally validate the TeleStroke Mimic (TM) score in a broader population. METHODS: We evaluated the TM score in 1930 telestroke consults from the University of Utah, Georgia Regents University, and the German TeleMedical Project for Integrative Stroke Care Network. We report the area under the curve in receiver-operating characteristic curve analysis with 95% confidence interval for our previously derived TM score in which lower TM scores correspond with a higher likelihood of being a stroke mimic. RESULTS: Based on final diagnosis at the end of the telestroke consultation, there were 630 of 1930 (32.6%) stroke mimics in the external validation cohort. All 6 variables included in the score were significantly different between patients with ischemic cerebrovascular disease versus stroke mimics. The TM score performed well (area under curve, 0.72; 95% confidence interval, 0.70-0.73; P<0.001), similar to our prior external validation in the Partners National Telestroke Network. CONCLUSIONS: The TM score's ability to predict the presence of a stroke mimic during telestroke consultation in these diverse cohorts was similar to its performance in our original cohort. Predictive decision-support tools like the TM score may help highlight key clinical differences between mimics and patients with stroke during complex, time-critical telestroke evaluations.
Authors: H Ay; F S Buonanno; G Rordorf; P W Schaefer; L H Schwamm; O Wu; R G Gonzalez; K Yamada; G A Sorensen; W J Koroshetz Journal: Neurology Date: 1999-06-10 Impact factor: 9.910
Authors: Alejandro M Brunser; Sergio Illanes; Pablo M Lavados; Paula Muñoz; Daniel Cárcamo; Arnold Hoppe; Verónica V Olavarria; Iris Delgado; Violeta Díaz Journal: J Stroke Cerebrovasc Dis Date: 2012-12-14 Impact factor: 2.136
Authors: Jeffrey A Switzer; Bart M Demaerschalk; Jipan Xie; Liangyi Fan; Kathleen F Villa; Eric Q Wu Journal: Circ Cardiovasc Qual Outcomes Date: 2012-12-04
Authors: David T Winkler; Felix Fluri; Peter Fuhr; Stephan G Wetzel; Philippe A Lyrer; Stephan Ruegg; Stefan T Engelter Journal: Stroke Date: 2009-01-22 Impact factor: 7.914
Authors: Mohammad Anadani; Eyad Almallouhi; Amy E Wahlquist; Ellen Debenham; Christine A Holmstedt Journal: Telemed J E Health Date: 2019-02-12 Impact factor: 3.536
Authors: Paul M Wechsler; Neal S Parikh; Linda A Heier; Evelyn Ruiz; Matthew E Fink; Babak B Navi; Halina White Journal: Neurohospitalist Date: 2021-03-29
Authors: Sunil A Sheth; Tzu-Ching Wu; Anjail Sharrief; Christy Ankrom; James C Grotta; Marc Fisher; Sean I Savitz Journal: Stroke Date: 2020-05-18 Impact factor: 7.914
Authors: Bronwyn Tunnage; Lisa J Woodhouse; Mark Dixon; Craig Anderson; Sandeep Ankolekar; Jason Appleton; Lesley Cala; Timothy England; Kailash Krishnan; Diane Havard; Grant Mair; Keith Muir; Steve Phillips; John Potter; Christopher Price; Marc Randall; Thompson G Robinson; Christine Roffe; Else Sandset; Niro Siriwardena; Polly Scutt; Joanna M Wardlaw; Nikola Sprigg; Philip M Bath Journal: BMC Emerg Med Date: 2022-01-10
Authors: Lucinda Tran; Longting Lin; Neil Spratt; Andrew Bivard; Beng Lim Alvin Chew; James W Evans; William O'Brien; Christopher Levi; Timothy Ang; Khaled Alanati; Elizabeth Pepper; Carlos Garcia-Esperon; Mark Parsons Journal: Front Neurol Date: 2021-12-03 Impact factor: 4.003
Authors: J Barlinn; S Winzer; H Worthmann; C Urbanek; K G Häusler; A Günther; H Erdur; M Görtler; L Busetto; C Wojciechowski; J Schmitt; Y Shah; B Büchele; P Sokolowski; T Kraya; S Merkelbach; B Rosengarten; K Stangenberg-Gliss; J Weber; F Schlachetzki; M Abu-Mugheisib; M Petersen; A Schwartz; F Palm; A Jowaed; B Volbers; P Zickler; J Remi; J Bardutzky; J Bösel; H J Audebert; G J Hubert; C Gumbinger Journal: Nervenarzt Date: 2021-05-27 Impact factor: 1.214