Frontiers in Neurology (Oct 2018)

A Practical Score for Prediction of Outcome After Cerebral Venous Thrombosis

  • Miguel A. Barboza,
  • Miguel A. Barboza,
  • Erwin Chiquete,
  • Antonio Arauz,
  • Marlon Merlos-Benitez,
  • Alejandro Quiroz-Compeán,
  • Fernando Barinagarrementería,
  • Carlos Cantú-Brito

DOI
https://doi.org/10.3389/fneur.2018.00882
Journal volume & issue
Vol. 9

Abstract

Read online

Background: Most patients with cerebral venous thrombosis (CVT) have independent survival in the short term. However, identification of high-risk individuals with an unfavorable outcome is a challenging task. We aimed to develop a CVT grading scale (CVT-GS) to aid in the short-term clinical decision-making.Methods: We included 467 consecutive patients with CVT who were hospitalized from 1981 to 2015 in two third-level referral hospitals. Factors associated with 30-day mortality were selected with bivariate analyses to integrate a Cox proportional-hazards model to determine components of the final scoring. After the scale was configured, the prognostic performance was tested for prediction of short-term death or moderately impaired to death [modified Rankin scale (mRS) > 2]. CVT-GS was categorized as mild, moderate or severe for the prediction of 30-day fatality rate and a probability of mRS > 2.Results: The 30-day case fatality rate was 9.0%. The CVT-GS (0–13 points; more points predicting poorer outcomes) was composed of parenchymal lesion size > 6 cm (3 points), bilateral Babinski signs (3 points), male sex (2 points), parenchymal hemorrhage (2 points), and level of consciousness (coma: 3 points, stupor: 2, somnolence: 1, and alert: 0). CVT was categorized as mild (0–2 points, 0.4% fatality rate), moderate (3–7 points, 9.9% fatality rate), or severe (8–13 points, 61.4% fatality rate). The CVT-GS had an accuracy of 91.6% for the prediction of 30-day mortality and 85.3% for mRS > 2.Conclusions: CVT-GS is a practical clinical tool for prediction of outcome after CVT. This score may aid in clinical decision-making and could serve to stratify patients enrolled in clinical trials.

Keywords