BACKGROUND AND PURPOSE: Various clinical, laboratory, and radiographic parameters have been identified as predictors of outcome for ischemic stroke. The purpose of this study was to combine these parameters into a validated scale for outcome prognostication in patients with a middle cerebral artery territory infarction. METHODS: We retrospectively reviewed 129 patients over a 2-year period and considered demographic, clinical, laboratory, and radiographic parameters as potential predictors of outcome. Inclusion criteria were unilateral hemispheric infarcts within the middle cerebral artery territory >15 mm in diameter. Our primary outcome measure was a favorable recovery defined as a modified Rankin Score was ≤2 at 30 days. A multivariable model was used to determine independent predictors of outcome and weighted to create a 5-item scale to predict stroke recovery. External validation of this model was done using data from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) study. RESULTS: The 5 independent predictors of outcome were as follows: age (OR, 1.09; 95% CI, 1.03 to 1.14; P=0.001), National Institutes of Health Stroke Scale score (OR, 1.17; 95% CI, 1.06 to 1.30; P=0.003), infarct volume (OR, 1.01; 95% CI, 1.00 to 1.02; P=0.03), admission white blood cell count (8.5×10(3)/mm(3); OR, 1.16; 95% CI, 1.03 to 1.27; P=0.04), and presence of hyperglycemia (OR, 4.2; 95% CI, 1.1 to 16.4; P=0.04). Combining these variables into a point scale significantly improved prediction over the individual variables accounted alone as evidenced by the area underneath the receiver operating curve (OR, 0.91; 95% CI, 0.87 to 0.96; P=0.0001). When applied to the DEFUSE study population for validation, the model achieved a sensitivity of 83% and specificity of 86%. CONCLUSIONS: With validation from a prospective study of similar patients, this model serves as a useful clinical and research tool to predict stroke recovery after cortical middle cerebral artery territory infarction.
BACKGROUND AND PURPOSE: Various clinical, laboratory, and radiographic parameters have been identified as predictors of outcome for ischemic stroke. The purpose of this study was to combine these parameters into a validated scale for outcome prognostication in patients with a middle cerebral artery territory infarction. METHODS: We retrospectively reviewed 129 patients over a 2-year period and considered demographic, clinical, laboratory, and radiographic parameters as potential predictors of outcome. Inclusion criteria were unilateral hemispheric infarcts within the middle cerebral artery territory >15 mm in diameter. Our primary outcome measure was a favorable recovery defined as a modified Rankin Score was ≤2 at 30 days. A multivariable model was used to determine independent predictors of outcome and weighted to create a 5-item scale to predict stroke recovery. External validation of this model was done using data from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) study. RESULTS: The 5 independent predictors of outcome were as follows: age (OR, 1.09; 95% CI, 1.03 to 1.14; P=0.001), National Institutes of Health Stroke Scale score (OR, 1.17; 95% CI, 1.06 to 1.30; P=0.003), infarct volume (OR, 1.01; 95% CI, 1.00 to 1.02; P=0.03), admission white blood cell count (8.5×10(3)/mm(3); OR, 1.16; 95% CI, 1.03 to 1.27; P=0.04), and presence of hyperglycemia (OR, 4.2; 95% CI, 1.1 to 16.4; P=0.04). Combining these variables into a point scale significantly improved prediction over the individual variables accounted alone as evidenced by the area underneath the receiver operating curve (OR, 0.91; 95% CI, 0.87 to 0.96; P=0.0001). When applied to the DEFUSE study population for validation, the model achieved a sensitivity of 83% and specificity of 86%. CONCLUSIONS: With validation from a prospective study of similar patients, this model serves as a useful clinical and research tool to predict stroke recovery after cortical middle cerebral artery territory infarction.
Authors: S Prabhakaran; M Soltanolkotabi; A R Honarmand; R A Bernstein; V H Lee; J J Conners; F Dehkordi-Vakil; A Shaibani; M C Hurley; S A Ansari Journal: AJNR Am J Neuroradiol Date: 2014-03-27 Impact factor: 3.825
Authors: Neil F Rosenberg; Storm M Liebling; Adam R Kosteva; Matthew B Maas; Shyam Prabhakaran; Andrew M Naidech Journal: Neurocrit Care Date: 2013-12 Impact factor: 3.210
Authors: Shyam Prabhakaran; Tudor G Jovin; Ashis H Tayal; Muhammad S Hussain; Thanh N Nguyen; Kevin N Sheth; John B Terry; Raul G Nogueira; Anat Horev; Dheeraj Gandhi; Dolora Wisco; Brenda A Glenn; Bryan Ludwig; Paul F Clemmons; Carolyn A Cronin; Melissa Tian; David Liebeskind; Osama O Zaidat; Alicia C Castonguay; Coleman Martin; Nils Mueller-Kronast; Joey D English; Italo Linfante; Timothy W Malisch; Rishi Gupta Journal: Cerebrovasc Dis Date: 2014-06-18 Impact factor: 2.762