BACKGROUND: There is currently no instrument to stratify patients presenting with ischemic stroke according to early risk of recurrent stroke. We sought to develop a comprehensive prognostic score to predict 90-day risk of recurrent stroke. METHODS: We analyzed data on 1,458 consecutive ischemic stroke patients using a Cox regression model with time to recurrent stroke as the response and clinical and imaging features typically available to physician at admission as covariates. The 90-day risk of recurrent stroke was calculated by summing up the number of independent predictors weighted by their corresponding beta-coefficients. The resultant score was called recurrence risk estimator at 90 days or RRE-90 score (available at: http://www.nmr.mgh.harvard.edu/RRE-90/). RESULTS: Sixty recurrent strokes (54 had baseline imaging) occurred during the follow-up period. The risk adjusted for time to follow-up was 6.0%. Predictors of recurrence included admission etiologic stroke subtype, prior history of TIA/stroke, and topography, age, and distribution of brain infarcts. The RRE-90 score demonstrated adequate calibration and good discrimination (area under the ROC curve [AUC] = 0.70-0.80), which was maintained when applied to a separate cohort of 433 patients (AUC = 0.70-0.76). The model's performance was also maintained for predicting early (14-day) risk of recurrence (AUC = 0.80). CONCLUSIONS: The RRE-90 is a Web-based, easy-to-use prognostic score that integrates clinical and imaging information available in the acute setting to quantify early risk of recurrent stroke. The RRE-90 demonstrates good predictive performance, suggesting that, if validated externally, it has promise for use in creating individualized patient management algorithms and improving clinical practice in acute stroke care.
BACKGROUND: There is currently no instrument to stratify patients presenting with ischemic stroke according to early risk of recurrent stroke. We sought to develop a comprehensive prognostic score to predict 90-day risk of recurrent stroke. METHODS: We analyzed data on 1,458 consecutive ischemic strokepatients using a Cox regression model with time to recurrent stroke as the response and clinical and imaging features typically available to physician at admission as covariates. The 90-day risk of recurrent stroke was calculated by summing up the number of independent predictors weighted by their corresponding beta-coefficients. The resultant score was called recurrence risk estimator at 90 days or RRE-90 score (available at: http://www.nmr.mgh.harvard.edu/RRE-90/). RESULTS: Sixty recurrent strokes (54 had baseline imaging) occurred during the follow-up period. The risk adjusted for time to follow-up was 6.0%. Predictors of recurrence included admission etiologic stroke subtype, prior history of TIA/stroke, and topography, age, and distribution of brain infarcts. The RRE-90 score demonstrated adequate calibration and good discrimination (area under the ROC curve [AUC] = 0.70-0.80), which was maintained when applied to a separate cohort of 433 patients (AUC = 0.70-0.76). The model's performance was also maintained for predicting early (14-day) risk of recurrence (AUC = 0.80). CONCLUSIONS: The RRE-90 is a Web-based, easy-to-use prognostic score that integrates clinical and imaging information available in the acute setting to quantify early risk of recurrent stroke. The RRE-90 demonstrates good predictive performance, suggesting that, if validated externally, it has promise for use in creating individualized patient management algorithms and improving clinical practice in acute stroke care.
Authors: W N Kernan; C M Viscoli; L M Brass; R W Makuch; P M Sarrel; R S Roberts; M Gent; P Rothwell; R L Sacco; R C Liu; B Boden-Albala; R I Horwitz Journal: Stroke Date: 2000-02 Impact factor: 7.914
Authors: Ralph L Sacco; Robert Adams; Greg Albers; Mark J Alberts; Oscar Benavente; Karen Furie; Larry B Goldstein; Philip Gorelick; Jonathan Halperin; Robert Harbaugh; S Claiborne Johnston; Irene Katzan; Margaret Kelly-Hayes; Edgar J Kenton; Michael Marks; Lee H Schwamm; Thomas Tomsick Journal: Stroke Date: 2006-02 Impact factor: 7.914
Authors: Hakan Ay; Jamary Oliveira-Filho; Ferdinando S Buonanno; Pamela W Schaefer; Karen L Furie; Yuchiao Chang Chang; Guy Rordorf; Lee H Schwamm; R Gilberto Gonzalez; Walter J Koroshetz Journal: Cerebrovasc Dis Date: 2002 Impact factor: 2.762
Authors: M G Lansberg; V N Thijs; M W O'Brien; J O Ali; A J de Crespigny; D C Tong; M E Moseley; G W Albers Journal: AJNR Am J Neuroradiol Date: 2001-04 Impact factor: 3.825
Authors: M J Alberts; G Hademenos; R E Latchaw; A Jagoda; J R Marler; M R Mayberg; R D Starke; H W Todd; K M Viste; M Girgus; T Shephard; M Emr; P Shwayder; M D Walker Journal: JAMA Date: 2000-06-21 Impact factor: 56.272
Authors: Thomas Hillen; Catherine Coshall; Kate Tilling; Anthony G Rudd; Rory McGovern; Charles D A Wolfe Journal: Stroke Date: 2003-05-15 Impact factor: 7.914
Authors: Ava L Liberman; Ali Zandieh; Caitlin Loomis; Jonathan M Raser-Schramm; Christina A Wilson; Jose Torres; Koto Ishida; Swaroop Pawar; Rebecca Davis; Michael T Mullen; Steven R Messé; Scott E Kasner; Brett L Cucchiara Journal: Stroke Date: 2017-01-11 Impact factor: 7.914
Authors: M F Giles; G W Albers; P Amarenco; E M Arsava; A W Asimos; H Ay; D Calvet; S B Coutts; B L Cucchiara; A M Demchuk; S C Johnston; P J Kelly; A S Kim; J Labreuche; P C Lavallee; J-L Mas; A Merwick; J M Olivot; F Purroy; W D Rosamond; R Sciolla; P M Rothwell Journal: Neurology Date: 2011-08-24 Impact factor: 9.910
Authors: E Murat Arsava; Johanna Helenius; Ross Avery; Mine H Sorgun; Gyeong-Moon Kim; Octavio M Pontes-Neto; Kwang Yeol Park; Jonathan Rosand; Mark Vangel; Hakan Ay Journal: JAMA Neurol Date: 2017-04-01 Impact factor: 18.302
Authors: Shadi Yaghi; Sara K Rostanski; Amelia K Boehme; Sheryl Martin-Schild; Alyana Samai; Brian Silver; Christina A Blum; Mahesh V Jayaraman; Matthew S Siket; Muhib Khan; Karen L Furie; Mitchell S V Elkind; Randolph S Marshall; Joshua Z Willey Journal: JAMA Neurol Date: 2016-05-01 Impact factor: 18.302