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January 01, 2013; 80 (1) Article

Stroke Prognostication using Age and NIH Stroke Scale

SPAN-100

Gustavo Saposnik, Amy K. Guzik, Mathew Reeves, Bruce Ovbiagele, S. Claiborne Johnston
First published November 21, 2012, DOI: https://doi.org/10.1212/WNL.0b013e31827b1ace
Gustavo Saposnik
From the Stroke Outcomes Research Unit (G.S.), Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; University of San Diego (A.K.G.), San Diego, CA; Department of Epidemiology and Biostatistics (M.R.), Michigan State University, East Lansing; Department of Neurosciences (B.O.), Medical University of South Carolina, Charleston; Clinical and Translational Science Institute and Department of Neurology (S.C.J.), University of California, San Francisco; Institute for Clinical Evaluative Sciences (ICES) (G.S.), Toronto; and Institute of Health Policy, Management and Evaluation (iHPME) 9G.S.0, University of Toronto, Canada.
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Amy K. Guzik
From the Stroke Outcomes Research Unit (G.S.), Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; University of San Diego (A.K.G.), San Diego, CA; Department of Epidemiology and Biostatistics (M.R.), Michigan State University, East Lansing; Department of Neurosciences (B.O.), Medical University of South Carolina, Charleston; Clinical and Translational Science Institute and Department of Neurology (S.C.J.), University of California, San Francisco; Institute for Clinical Evaluative Sciences (ICES) (G.S.), Toronto; and Institute of Health Policy, Management and Evaluation (iHPME) 9G.S.0, University of Toronto, Canada.
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Mathew Reeves
From the Stroke Outcomes Research Unit (G.S.), Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; University of San Diego (A.K.G.), San Diego, CA; Department of Epidemiology and Biostatistics (M.R.), Michigan State University, East Lansing; Department of Neurosciences (B.O.), Medical University of South Carolina, Charleston; Clinical and Translational Science Institute and Department of Neurology (S.C.J.), University of California, San Francisco; Institute for Clinical Evaluative Sciences (ICES) (G.S.), Toronto; and Institute of Health Policy, Management and Evaluation (iHPME) 9G.S.0, University of Toronto, Canada.
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Bruce Ovbiagele
From the Stroke Outcomes Research Unit (G.S.), Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; University of San Diego (A.K.G.), San Diego, CA; Department of Epidemiology and Biostatistics (M.R.), Michigan State University, East Lansing; Department of Neurosciences (B.O.), Medical University of South Carolina, Charleston; Clinical and Translational Science Institute and Department of Neurology (S.C.J.), University of California, San Francisco; Institute for Clinical Evaluative Sciences (ICES) (G.S.), Toronto; and Institute of Health Policy, Management and Evaluation (iHPME) 9G.S.0, University of Toronto, Canada.
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S. Claiborne Johnston
From the Stroke Outcomes Research Unit (G.S.), Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; University of San Diego (A.K.G.), San Diego, CA; Department of Epidemiology and Biostatistics (M.R.), Michigan State University, East Lansing; Department of Neurosciences (B.O.), Medical University of South Carolina, Charleston; Clinical and Translational Science Institute and Department of Neurology (S.C.J.), University of California, San Francisco; Institute for Clinical Evaluative Sciences (ICES) (G.S.), Toronto; and Institute of Health Policy, Management and Evaluation (iHPME) 9G.S.0, University of Toronto, Canada.
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Citation
Stroke Prognostication using Age and NIH Stroke Scale
SPAN-100
Gustavo Saposnik, Amy K. Guzik, Mathew Reeves, Bruce Ovbiagele, S. Claiborne Johnston
Neurology Jan 2013, 80 (1) 21-28; DOI: 10.1212/WNL.0b013e31827b1ace

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ABSTRACT

Objectives: Age and stroke severity are major determinants of stroke outcomes, but systematically incorporating these prognosticators in the routine practice of acute ischemic stroke can be challenging. We evaluated the effect of an index combining age and stroke severity on response to IV tissue plasminogen activator (tPA) among patients in the National Institute of Neurological Disorders and Stroke (NINDS) tPA stroke trials.

Methods: We created the Stroke Prognostication using Age and NIH Stroke Scale (SPAN) index by combining age in years plus NIH Stroke Scale (NIHSS) ≥100. We applied the SPAN-100 index to patients in the NINDS tPA stroke trials (parts I and II) to evaluate its ability to predict clinical response and risk of intracerebral hemorrhage (ICH) after thrombolysis. The main outcome measures included ICH (any type) and a composite favorable outcome (defined as a modified Rankin Scale score of 0 or 1, NIHSS ≤1, Barthel index ≥95, and Glasgow Outcome Scale score of 1) at 3 months. Bivariate and multivariable logistic regression analyses were used to determine the association between SPAN-100 and outcomes of interest.

Results: Among 624 patients in the NINDS trials, 62 (9.9%) participants were SPAN-100 positive. Among those receiving tPA, ICH rates were higher for SPAN-100–positive patients (42% vs 12% in SPAN-100–negative patients; p < 0.001); similarly, ICH rates were higher in SPAN-100–positive patients (19% vs 5%; p = 0.005) among those not receiving tPA. SPAN-100 was associated with worse outcomes. The benefit of tPA, defined as favorable composite outcome at 3 months, was present in SPAN-100–negative patients (55.4% vs 40.2%; p < 0.001), but not in SPAN-100–positive patients (5.6% tPA vs 3.9%; p = 0.76). Similar trends were found for secondary outcomes (e.g., symptomatic ICH, catastrophic outcome, discharge home).

Conclusion: The SPAN-100 index could be a simple method for estimating the clinical response and risk of hemorrhagic complications after tPA for acute ischemic stroke. These results need further confirmation in larger contemporary datasets.

GLOSSARY

AR=
attributable risk;
AUC=
area under the curve;
CI=
confidence interval;
ECASS=
European Cooperative Acute Stroke Study;
HAT=
Hemorrhage after Thrombolysis;
ICH=
intracerebral hemorrhage;
mRS=
modified Rankin Scale;
NIHSS=
NIH Stroke Scale;
NINDS=
National Institute of Neurological Disorders and Stroke;
NNH=
number needed to harm;
NNT=
number needed to treat;
OR=
odds ratio;
PAR=
population attributable risk;
ROC=
receiver operating characteristic;
sICH=
symptomatic ICH;
SPAN=
Stroke Prognostication using Age and NIH Stroke Scale;
tPA=
tissue plasminogen activator;
UCSD=
University of California, San Diego

Footnotes

  • Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Editorial, page 15

  • Received May 24, 2012.
  • Accepted August 15, 2012.
  • © 2012 American Academy of Neurology
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Letters: Rapid online correspondence

  • Response to Dr. Yufe
    • Gustavo Saposnik, Directo, Stroke Outcomes Research Center, St Michael's Hospital, University of Torontosaposnikg@smh.ca
    Submitted January 11, 2013
  • Stroke Prognostication using Age and NIH Stroke Scale: SPAN-100
    • Robert Yufe, Physician, William Osler Health Systemrobertyufe@yahoo.ca
    Submitted January 10, 2013
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