Improving Efficiency of Stroke Research: The Brain Attack Surveillance in Corpus Christi Study
Document Type
Article
Publication Date
2003
Keywords
Stroke, Epidemiology, Screening, Surveillance, Computer, Clinical
Digital Object Identifier (DOI)
https://doi.org/10.1016/S0895-4356(03)00005-2
Abstract
We studied whether a computer algorithm or abstractor could diagnose stroke as well as a fellowship-trained stroke neurologist. As part of an ongoing prospective, community-based stroke surveillance project, a diagnostic algorithm was developed, and patients' neurologic signs and symptoms were collected in a computerized database. The abstractors were blinded to the results of this algorithm and were asked to verify whether the patient had a stroke. The separate results of the computer and abstractor were compared with the final diagnosis given by the blinded neurologist. From 1 January through 31 July 2000, 3418 cases were screened. The abstractors yielded sensitivity 91%, specificity 97%, positive predictive value (PPV) 85%, and negative predictive value (NPV) 99%. Three computer algorithms were evaluated. The sensitivities ranged from 83% to 96%, specificity ranged from 88% to 97%, PPV ranged from 54% to 81%, and NPV ranged from 97% to 99%. The use of computer verification or abstractors may obviate the need for physician stroke verification and may greatly improve study efficiency.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Journal of Clinical Epidemiology, v. 56, issue 4, p. 351-357
Scholar Commons Citation
Al-Wabil, Areej; Smith, Melinda A.; Moyé, Lemuel A.; Burgin, W. Scott; and Morgenstern, Lewis B., "Improving Efficiency of Stroke Research: The Brain Attack Surveillance in Corpus Christi Study" (2003). Neurology Faculty Publications. 65.
https://digitalcommons.usf.edu/neur_facpub/65
