BACKGROUND AND PURPOSE: NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. METHODS: Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. RESULTS: The distribution of pathogenic categories varied by study, age, sex, and race (P<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69-0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70-0.75). CONCLUSIONS: This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.
BACKGROUND AND PURPOSE: NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. METHODS: Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. RESULTS: The distribution of pathogenic categories varied by study, age, sex, and race (P<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69-0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70-0.75). CONCLUSIONS: This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.
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Authors: Andreas Gschwendtner; Steve Bevan; John W Cole; Anna Plourde; Mar Matarin; Helen Ross-Adams; Thomas Meitinger; Erich Wichmann; Braxton D Mitchell; Karen Furie; Agnieszka Slowik; Stephen S Rich; Paul D Syme; Mary J MacLeod; James F Meschia; Jonathan Rosand; Steve J Kittner; Hugh S Markus; Bertram Müller-Myhsok; Martin Dichgans Journal: Ann Neurol Date: 2009-05 Impact factor: 10.422
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Authors: Elizabeth G Holliday; Jane M Maguire; Tiffany-Jane Evans; Simon A Koblar; Jim Jannes; Jonathan W Sturm; Graeme J Hankey; Ross Baker; Jonathan Golledge; Mark W Parsons; Rainer Malik; Mark McEvoy; Erik Biros; Martin D Lewis; Lisa F Lincz; Roseanne Peel; Christopher Oldmeadow; Wayne Smith; Pablo Moscato; Simona Barlera; Steve Bevan; Joshua C Bis; Eric Boerwinkle; Giorgio B Boncoraglio; Thomas G Brott; Robert D Brown; Yu-Ching Cheng; John W Cole; Ioana Cotlarciuc; William J Devan; Myriam Fornage; Karen L Furie; Sólveig Grétarsdóttir; Andreas Gschwendtner; M Arfan Ikram; W T Longstreth; James F Meschia; Braxton D Mitchell; Thomas H Mosley; Michael A Nalls; Eugenio A Parati; Bruce M Psaty; Pankaj Sharma; Kari Stefansson; Gudmar Thorleifsson; Unnur Thorsteinsdottir; Matthew Traylor; Benjamin F J Verhaaren; Kerri L Wiggins; Bradford B Worrall; Cathie Sudlow; Peter M Rothwell; Martin Farrall; Martin Dichgans; Jonathan Rosand; Hugh S Markus; Rodney J Scott; Christopher Levi; John Attia Journal: Nat Genet Date: 2012-09-02 Impact factor: 38.330
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Authors: Clinton B Wright; Alexander P Auchus; Alan Lerner; Walter T Ambrosius; Hakan Ay; Jeffrey T Bates; Jing Chen; James F Meschia; Suchita Pancholi; Vasilios Papademetriou; Anjay Rastogi; Mary Sweeney; James J Willard; Jerry Yee; Suzanne Oparil Journal: Hypertension Date: 2021-02-15 Impact factor: 10.190
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