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....
Autores principales: | , , , , , , , , , , |
---|---|
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 |