A scoring tool to predict mortality and dependency after cerebral venous thrombosis


Lindgren E., Krzywicka K., de Winter M. A., Sánchez Van Kammen M., Heldner M. R., Hiltunen S., ...More

European Journal of Neurology, vol.30, no.8, pp.2305-2314, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 8
  • Publication Date: 2023
  • Doi Number: 10.1111/ene.15844
  • Journal Name: European Journal of Neurology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, EMBASE, MEDLINE, Psycinfo
  • Page Numbers: pp.2305-2314
  • Keywords: cerebral venous thrombosis, dependency, follow-up, mortality, outcome, prognosis, risk score, stroke
  • Istanbul University Affiliated: Yes

Abstract

Background and purpose: A prognostic score was developed to predict dependency and death after cerebral venous thrombosis (CVT) to identify patients for targeted therapy in future clinical trials. Methods: Data from the International CVT Consortium were used. Patients with pre-existent functional dependency were excluded. Logistic regression was used to predict poor outcome (modified Rankin Scale score 3–6) at 6 months and Cox regression to predict 30-day and 1-year all-cause mortality. Potential predictors derived from previous studies were selected with backward stepwise selection. Coefficients were shrunk using ridge regression to adjust for optimism in internal validation. Results: Of 1454 patients with CVT, the cumulative number of deaths was 44 (3%) and 70 (5%) for 30 days and 1 year, respectively. Of 1126 patients evaluated regarding functional outcome, 137 (12%) were dependent or dead at 6 months. From the retained predictors for both models, the SI2NCAL2C score was derived utilizing the following components: absence of female-sex-specific risk factor, intracerebral hemorrhage, infection of the central nervous system, neurological focal deficits, coma, age, lower level of hemoglobin (g/l), higher level of glucose (mmol/l) at admission, and cancer. C-statistics were 0.80 (95% confidence interval [CI] 0.75–0.84), 0.84 (95% CI 0.80–0.88) and 0.84 (95% CI 0.80–0.88) for the poor outcome, 30-day and 1-year mortality model, respectively. Calibration plots indicated a good model fit between predicted and observed values. The SI2NCAL2C score calculator is freely available at www.cerebralvenousthrombosis.com. Conclusions: The SI2NCAL2C score shows adequate performance for estimating individual risk of mortality and dependency after CVT but external validation of the score is warranted.