BACKGROUND AND AIMS: The Houston Intra-Arterial Therapy score predicts poor functional outcome following endovascular treatment for acute ischemic stroke based on clinical variables. The present study sought to (a) create a predictive scoring system that included a neuroimaging variable and (b) determine if the scoring systems predict the clinical response to reperfusion. METHODS: Separate datasets were used to derive (n = 110 from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2 study) and validate (n = 125 from Massachusetts General Hospital) scoring systems that predict poor functional outcome, defined as a modified Rankin Scale score of 4-6 at 90 days. RESULTS: Age (P < 0·001; β = 0·087) and diffusion-weighted imaging volume (P = 0·023; β = 0·025) were the independent predictors of poor functional outcome. The Stanford Age and Diffusion-Weighted Imaging score was created based on the patient's age (0-3 points) and diffusion-weighted imaging lesion volume (0-1 points). The percentage of patients with a poor functional outcome increased significantly with the number of points on the Stanford Age and Diffusion-Weighted Imaging score (P < 0·01 for trend). The area under the receiver operating characteristic curve for the Stanford Age and Diffusion-Weighted Imaging score was 0·82 in the derivation dataset. In the validation cohort, the area under the receiver operating characteristic curve was 0·69 for the Stanford Age and Diffusion-Weighted Imaging score and 0·66 for the Houston Intra-Arterial Therapy score (P = 0·45 for the difference). Reperfusion, but not the interactions between the prediction scores and reperfusion, were predictors of outcome (P > 0·5). CONCLUSIONS: The Stanford Age and Diffusion-Weighted Imaging and Houston Intra-Arterial Therapy scores can be used to predict poor functional outcome following endovascular therapy with good accuracy. However, these scores do not predict the clinical response to reperfusion. This limits their utility as tools to select patients for acute stroke interventions.
BACKGROUND AND AIMS: The Houston Intra-Arterial Therapy score predicts poor functional outcome following endovascular treatment for acute ischemic stroke based on clinical variables. The present study sought to (a) create a predictive scoring system that included a neuroimaging variable and (b) determine if the scoring systems predict the clinical response to reperfusion. METHODS: Separate datasets were used to derive (n = 110 from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2 study) and validate (n = 125 from Massachusetts General Hospital) scoring systems that predict poor functional outcome, defined as a modified Rankin Scale score of 4-6 at 90 days. RESULTS: Age (P < 0·001; β = 0·087) and diffusion-weighted imaging volume (P = 0·023; β = 0·025) were the independent predictors of poor functional outcome. The Stanford Age and Diffusion-Weighted Imaging score was created based on the patient's age (0-3 points) and diffusion-weighted imaging lesion volume (0-1 points). The percentage of patients with a poor functional outcome increased significantly with the number of points on the Stanford Age and Diffusion-Weighted Imaging score (P < 0·01 for trend). The area under the receiver operating characteristic curve for the Stanford Age and Diffusion-Weighted Imaging score was 0·82 in the derivation dataset. In the validation cohort, the area under the receiver operating characteristic curve was 0·69 for the Stanford Age and Diffusion-Weighted Imaging score and 0·66 for the Houston Intra-Arterial Therapy score (P = 0·45 for the difference). Reperfusion, but not the interactions between the prediction scores and reperfusion, were predictors of outcome (P > 0·5). CONCLUSIONS: The Stanford Age and Diffusion-Weighted Imaging and Houston Intra-Arterial Therapy scores can be used to predict poor functional outcome following endovascular therapy with good accuracy. However, these scores do not predict the clinical response to reperfusion. This limits their utility as tools to select patients for acute stroke interventions.
Authors: Maarten G Lansberg; Jun Lee; Soren Christensen; Matus Straka; Deidre A De Silva; Michael Mlynash; Bruce C Campbell; Roland Bammer; Jean-Marc Olivot; Patricia Desmond; Stephen M Davis; Geoffrey A Donnan; Gregory W Albers Journal: Stroke Date: 2011-04-14 Impact factor: 7.914
Authors: Albert J Yoo; Zeshan A Chaudhry; Raul G Nogueira; Michael H Lev; Pamela W Schaefer; Lee H Schwamm; Joshua A Hirsch; R Gilberto González Journal: Stroke Date: 2012-03-15 Impact factor: 7.914
Authors: Maarten G Lansberg; Matus Straka; Stephanie Kemp; Michael Mlynash; Lawrence R Wechsler; Tudor G Jovin; Michael J Wilder; Helmi L Lutsep; Todd J Czartoski; Richard A Bernstein; Cherylee W J Chang; Steven Warach; Franz Fazekas; Manabu Inoue; Aaryani Tipirneni; Scott A Hamilton; Greg Zaharchuk; Michael P Marks; Roland Bammer; Gregory W Albers Journal: Lancet Neurol Date: 2012-09-04 Impact factor: 44.182
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Authors: Hen Hallevi; Andrew D Barreto; David S Liebeskind; Miriam M Morales; Sheryl B Martin-Schild; Anitha T Abraham; Jignesh Gadia; Jeffrey L Saver; James C Grotta; Sean I Savitz Journal: Stroke Date: 2009-04-09 Impact factor: 7.914
Authors: Thoralf Thamm; Jia Guo; Jarrett Rosenberg; Tie Liang; Michael P Marks; Soren Christensen; Huy M Do; Stephanie M Kemp; Emma Adair; Irina Eyngorn; Michael Mlynash; Tudor G Jovin; Bart P Keogh; Hui J Chen; Maarten G Lansberg; Gregory W Albers; Greg Zaharchuk Journal: Stroke Date: 2019-10-17 Impact factor: 7.914
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