MGH Martinos Center for Biomedical Imaging 2017 | Page 37

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 disagree-
ments among physicians about the
cause. The CCS software helps to
reduce this complexity by exploit-
ing 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 consider-
ing 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.
The MGH team previously showed
that use of the CCS algorithm
reduced the disagreement rate
among physicians from 50 percent
to approximately 20 percent. The
recently published JAMA Neurology
study further established the utility
of the algorithm by demonstrating
its ability to generate categories of
etiologies with different clinical,
imaging and prognostic charac-
teristics for 1,816 ischemic stroke
patients enrolled in two previous
MGH-based studies. Based on
patient data, CCS was able to assign
etiologies to 20 to 40 percent of the
patients for which two other systems
were unable to determine a cause. It
also was better at determining the
likelihood of second stroke within
90 days.
“The validity data that have
emerged from the current study
add to the ut ility of the software-
based approach and highlight once
again that careful identification and
accurate classification of the under-
lying etiology is paramount for every
patient with stroke,” says Ay, who is
an associate professor of Radiology
at Harvard Medical School. “The
information the software provides
not only is critical for effective
stroke prevention but also could
increase the chances for new dis-
coveries 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 MGH-licensed CCS is available
at https://ccs.mgh.harvard.edu/ and
is free for academic use. The software
was designed to be a “living algo-
rithm” and can accommodate new
information as it emerges. New eti-
ology-specific biomarkers, genetic
markers, imaging markers and
clinical features that become avail-
able can be incorporated into the
existing CCS algorithm to further
enhance its ability to determine the
underlying causes of stroke.
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