Abstract
Cerebral Venous Sinus Thrombosis (CVST) is a stroke subtype with an incidence of 15 people per million per
year. CVST primarily affects children and young adults, especially young women of child-bearing age and those
who are at risk for hypercoagulability. The most common clinical symptoms at presentation include headaches
(90%) and seizures (40%). In more severe cases, focal deficits, depressed mental status and progression to
coma might occur. Systemic anticoagulation is the mainstay of the treatment, which is used for preventing
thrombus while facilitating recanalization. A substantial subset of patients may further deteriorate and at least
20% die or become disabled, with the highest mortality occurring during the acute phase of the illness. This
epidemiological landscape emphasizes the potential need of alternative acute therapeutic approaches to aid this
high-risk working-age CVST population.
Alternative therapies such as the use of new anticoagulants and/or Intravenous antiplatelet agents are promising
options in refractory patients when instituted early in the disease process. However, given the potential adverse
effects and elevated cost, an optimization of clinical decision tools that permit us to accurately stratify high-risk
CVST patients, represents the mandatory first step. To ameliorate this selection process, research efforts have
focused in the identification of clinical and radiological predictors to build a reliable prediction model.
Unfortunately, initial stratification scores have failed to demonstrate enough accuracy to be implemented into
clinical practice due to several conceptual constraints during model building and selection. A detail description
of the rigor of previous research will be presented in the significance section of this proposal.
After a thorough feasibility analysis of our CVST cohort at the University of Iowa, we are eager to propose a 2-
year study to develop and validate a new, simple, reproducible predictive score that will promptly stratify CVST
patients with poor outcome. The scale will be built on novel clinical and radiological biomarkers that appear
during early pathophysiological stages from two large CVST cohorts. We will also utilize innovative statistical
machine learning methodology to optimize our model selection and scale predictive performance. Finally, we will
evaluate our model in an independent subset of patients for further validation, refinement and generalizability.
Successful completion of this project will optimize the early selection of high-risk CVST patients before secondary
injury expands and perpetuates. Prior to implementation, an external validation of the scale in a larger multicenter
validation study will represent the first necessary critical step to open a window of opportunity to compare new
therapies against current clinical practice through RTCs in a significant group of young patients with otherwise
dismal outcome.
