BACKGROUND AND PURPOSE: An easily administered questionnaire and algorithm classifying transient ischemic attacks (TIAs) or strokes, and also their distribution, could be invaluable for identifying endpoints in epidemiologic studies or clinical trials of prevention and therapy of cerebral ischemia. The Asymptomatic Carotid Atherosclerosis Study (ACAS) devised a symptom-based questionnaire and algorithm for detecting events in the trial. The purpose of this study was to determine sensitivity, specificity, and agreement rates of the questionnaire and algorithm against diagnoses of a panel of cerebrovascular disease authorities. METHODS: Three hundred eighty-one men and women at eight medical centers reported symptoms of stroke, TIA, or other neurologic illness. The questionnaire was administered by trained interviewers and the responses were analyzed using the algorithm. A standardized neurologic examination was performed by a neurologist. Data were submitted to two or more external reviewers. Sensitivity, specificity, and the kappa statistic (kappa) were used to evaluate the relationship between the algorithm and the external reviewers' diagnosis. RESULTS: Of the 381 reviews, 196 were diagnosed as TIA or stroke by the external panel. The algorithm's agreement with the diagnosis of TIA or stroke was 80.1%, and kappa was 0.60. Sensitivity was 87.8%, and specificity was 71.9%. CONCLUSION: While statistical agreement rates depend on the method of sample selection, the algorithm has a high agreement with an external panel of experts and is a sensitive tool for event detection. The lower specificity indicates that careful neurologic evaluation may be required to confirm or refute events identified by the screening algorithm.
BACKGROUND AND PURPOSE: An easily administered questionnaire and algorithm classifying transient ischemic attacks (TIAs) or strokes, and also their distribution, could be invaluable for identifying endpoints in epidemiologic studies or clinical trials of prevention and therapy of cerebral ischemia. The Asymptomatic Carotid Atherosclerosis Study (ACAS) devised a symptom-based questionnaire and algorithm for detecting events in the trial. The purpose of this study was to determine sensitivity, specificity, and agreement rates of the questionnaire and algorithm against diagnoses of a panel of cerebrovascular disease authorities. METHODS: Three hundred eighty-one men and women at eight medical centers reported symptoms of stroke, TIA, or other neurologic illness. The questionnaire was administered by trained interviewers and the responses were analyzed using the algorithm. A standardized neurologic examination was performed by a neurologist. Data were submitted to two or more external reviewers. Sensitivity, specificity, and the kappa statistic (kappa) were used to evaluate the relationship between the algorithm and the external reviewers' diagnosis. RESULTS: Of the 381 reviews, 196 were diagnosed as TIA or stroke by the external panel. The algorithm's agreement with the diagnosis of TIA or stroke was 80.1%, and kappa was 0.60. Sensitivity was 87.8%, and specificity was 71.9%. CONCLUSION: While statistical agreement rates depend on the method of sample selection, the algorithm has a high agreement with an external panel of experts and is a sensitive tool for event detection. The lower specificity indicates that careful neurologic evaluation may be required to confirm or refute events identified by the screening algorithm.
Authors: Alexandra J Lansky; Steven R Messé; Adam M Brickman; Michael Dwyer; H Bart van der Worp; Ronald M Lazar; Cody G Pietras; Kevin J Abrams; Eugene McFadden; Nils H Petersen; Jeffrey Browndyke; Bernard Prendergast; Vivian G Ng; Donald E Cutlip; Samir Kapadia; Mitchell W Krucoff; Axel Linke; Claudia Scala Moy; Joachim Schofer; Gerrit-Anne van Es; Renu Virmani; Jeffrey Popma; Michael K Parides; Susheel Kodali; Michel Bilello; Robert Zivadinov; Joseph Akar; Karen L Furie; Daryl Gress; Szilard Voros; Jeffrey Moses; David Greer; John K Forrest; David Holmes; Arie P Kappetein; Michael Mack; Andreas Baumbach Journal: Eur Heart J Date: 2018-05-14 Impact factor: 29.983
Authors: Victor W Sung; Natasha Johnson; U Shanette Granstaff; William J Jones; James F Meschia; Linda S Williams; Monika M Safford Journal: Neuroepidemiology Date: 2011-02-10 Impact factor: 3.282
Authors: Melissa C Caughey; Laura R Loehr; Nigel S Key; Vimal K Derebail; Rebecca F Gottesman; Abhijit V Kshirsagar; Megan L Grove; Gerardo Heiss Journal: Stroke Date: 2014-08-19 Impact factor: 7.914
Authors: Matthew R Reynolds; Ashwin A Kamath; Robert L Grubb; William J Powers; Harold P Adams; Colin P Derdeyn Journal: J Neurol Neurosurg Psychiatry Date: 2013-11-18 Impact factor: 10.154
Authors: Melissa C Caughey; Ye Qiao; Beverly Gwen Windham; Rebecca F Gottesman; Thomas H Mosley; Bruce A Wasserman Journal: Am J Hypertens Date: 2018-07-16 Impact factor: 3.080