Washington: Determining the cause of an ischemic stroke is critical to prevent a second one and now, a new software-based system has offered hope.
Investigators at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital (MGH) and the MGH Stroke Service have developed a software package that provides evidence-based, automated support for diagnosing the cause of stroke.
“This was a much-needed study because, although stroke classifications systems are often used in research and clinical practice, these systems are not always able to produce subtypes with discrete pathophysiological, diagnostic and prognostic characteristics,” said senior author Hakan Ay.
Ay added, “We found that the CCS-based classifications provided better correlations between clinical and imaging stroke features and were better able to discriminate among stroke outcomes than were two conventional, non-automated classification methods.”
There are more than 150 different possible causes – or etiologies – of ischemic stroke, and approximately half of patients exhibit features suggesting more than one possible cause. This leads to considerable complexity in determining the cause of a stroke and, in roughly one of two patients, can lead to disagreements among physicians about the cause. The CCS software helps to reduce this complexity by exploiting classification criteria that are well defined, replicable and based on evidence rather than subjective assessment.
The CCS software does this in several ways. First, it weights the possible etiologies by considering the relative potential of each to cause a stroke. Second, in the presence of multiple potential causes it incorporates the clinical and imaging features that make one mechanism more probable than others for an individual patient. Third, it determines the likelihood of that cause by taking into account the number of diagnostic tests that were performed. And finally, it ensures that data is entered in a consistent manner. The software can also serve as an important research tool, by providing investigators with both the ability to examine how stroke etiologies interact with one another and the flexibility to define new etiology subtypes according to the needs of the individual research project.
Ay noted, “The information the software provides not only is critical for effective stroke prevention but also could increase the chances for new discoveries by enhancing the statistical power in future studies of etiologic stroke subtypes. We estimate that, compared to conventional systems, the use of CCS in stroke prevention trials testing targeted treatments for a particular etiologic subtype could reduce the required sample size by as much as 30 percent.”
The study is published online in JAMA Neurology. (ANI)