Cargando…

The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network

BACKGROUND: Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation....

Descripción completa

Detalles Bibliográficos
Autores principales: Ali, Syed F., Viswanathan, Anand, Singhal, Aneesh B., Rost, Natalia S., Forducey, Pamela G., Davis, Lawrence W., Schindler, Joseph, Likosky, William, Schlegel, Sherene, Solenski, Nina, Schwamm, Lee H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4309074/
https://www.ncbi.nlm.nih.gov/pubmed/24958778
http://dx.doi.org/10.1161/JAHA.114.000838
_version_ 1782354631944306688
author Ali, Syed F.
Viswanathan, Anand
Singhal, Aneesh B.
Rost, Natalia S.
Forducey, Pamela G.
Davis, Lawrence W.
Schindler, Joseph
Likosky, William
Schlegel, Sherene
Solenski, Nina
Schwamm, Lee H.
author_facet Ali, Syed F.
Viswanathan, Anand
Singhal, Aneesh B.
Rost, Natalia S.
Forducey, Pamela G.
Davis, Lawrence W.
Schindler, Joseph
Likosky, William
Schlegel, Sherene
Solenski, Nina
Schwamm, Lee H.
author_sort Ali, Syed F.
collection PubMed
description BACKGROUND: Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation. METHODS AND RESULTS: We analyzed 829 consecutive patients from January 2004 to April 2013 in our internal New England–based Partners TeleStroke Network for a derivation cohort, and 332 cases for internal validation. External validation was performed on 226 cases from January 2008 to August 2012 in the Partners National TeleStroke Network. A predictive score was developed using stepwise logistic regression, and its performance was assessed using receiver‐operating characteristic (ROC) curve analysis. There were 23% SM in the derivation, 24% in the internal, and 22% in external validation cohorts based on final clinical diagnosis. Compared to those with ischemic cerebrovascular disease (iCVD), SM had lower mean age, fewer vascular risk factors, more frequent prior seizure, and a different profile of presenting symptoms. The TeleStroke Mimic Score (TM‐Score) was based on factors independently associated with SM status including age, medical history (atrial fibrillation, hypertension, seizures), facial weakness, and National Institutes of Health Stroke Scale >14. The TM‐Score performed well on ROC curve analysis (derivation cohort AUC=0.75, internal validation AUC=0.71, external validation AUC=0.77). CONCLUSIONS: SMs differ substantially from their iCVD counterparts in their vascular risk profiles and other characteristics. Decision‐support tools based on predictive models, such as our TM Score, may help clinicians consider alternate diagnosis and potentially detect SMs during complex, time‐critical telestroke evaluations.
format Online
Article
Text
id pubmed-4309074
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Blackwell Publishing Ltd
record_format MEDLINE/PubMed
spelling pubmed-43090742015-01-28 The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network Ali, Syed F. Viswanathan, Anand Singhal, Aneesh B. Rost, Natalia S. Forducey, Pamela G. Davis, Lawrence W. Schindler, Joseph Likosky, William Schlegel, Sherene Solenski, Nina Schwamm, Lee H. J Am Heart Assoc Original Research BACKGROUND: Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation. METHODS AND RESULTS: We analyzed 829 consecutive patients from January 2004 to April 2013 in our internal New England–based Partners TeleStroke Network for a derivation cohort, and 332 cases for internal validation. External validation was performed on 226 cases from January 2008 to August 2012 in the Partners National TeleStroke Network. A predictive score was developed using stepwise logistic regression, and its performance was assessed using receiver‐operating characteristic (ROC) curve analysis. There were 23% SM in the derivation, 24% in the internal, and 22% in external validation cohorts based on final clinical diagnosis. Compared to those with ischemic cerebrovascular disease (iCVD), SM had lower mean age, fewer vascular risk factors, more frequent prior seizure, and a different profile of presenting symptoms. The TeleStroke Mimic Score (TM‐Score) was based on factors independently associated with SM status including age, medical history (atrial fibrillation, hypertension, seizures), facial weakness, and National Institutes of Health Stroke Scale >14. The TM‐Score performed well on ROC curve analysis (derivation cohort AUC=0.75, internal validation AUC=0.71, external validation AUC=0.77). CONCLUSIONS: SMs differ substantially from their iCVD counterparts in their vascular risk profiles and other characteristics. Decision‐support tools based on predictive models, such as our TM Score, may help clinicians consider alternate diagnosis and potentially detect SMs during complex, time‐critical telestroke evaluations. Blackwell Publishing Ltd 2014-06-23 /pmc/articles/PMC4309074/ /pubmed/24958778 http://dx.doi.org/10.1161/JAHA.114.000838 Text en © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial (http://creativecommons.org/licenses/by-nc/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Research
Ali, Syed F.
Viswanathan, Anand
Singhal, Aneesh B.
Rost, Natalia S.
Forducey, Pamela G.
Davis, Lawrence W.
Schindler, Joseph
Likosky, William
Schlegel, Sherene
Solenski, Nina
Schwamm, Lee H.
The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title_full The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title_fullStr The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title_full_unstemmed The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title_short The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network
title_sort telestroke mimic (tm)‐score: a prediction rule for identifying stroke mimics evaluated in a telestroke network
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4309074/
https://www.ncbi.nlm.nih.gov/pubmed/24958778
http://dx.doi.org/10.1161/JAHA.114.000838
work_keys_str_mv AT alisyedf thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT viswanathananand thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT singhalaneeshb thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT rostnatalias thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT forduceypamelag thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT davislawrencew thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schindlerjoseph thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT likoskywilliam thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schlegelsherene thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT solenskinina thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schwammleeh thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT thetelestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT alisyedf telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT viswanathananand telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT singhalaneeshb telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT rostnatalias telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT forduceypamelag telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT davislawrencew telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schindlerjoseph telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT likoskywilliam telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schlegelsherene telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT solenskinina telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT schwammleeh telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork
AT telestrokemimictmscoreapredictionruleforidentifyingstrokemimicsevaluatedinatelestrokenetwork