Skip to search boxSkip to navigationSkip to main content

Performance of Prognostic Risk Scores in Cerebral Venous Thrombosis: External Validation in the CLOT-VENUS Registry

*Corresponding author for this work
  • University of Iowa Health Care
    ,
  • Department of Neurology
    ,
  • UNAM
    ,
  • Instituto Nacional De Neurologia Y Neurocirugia
    ,
  • Doctoral Program in Medical Sciences
    ,
  • The University of Iowa
Research Output:
Contribution to journal
Article
Peer-review

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Article number

109649

Journal (Volume, Issue Number)

Clinical Neurology and Neurosurgery (Volume 272)

Publication milestones

  • Published - 01/2027

Publication status

Published - 01/2027

ISSN

0303-8467

Publication IDs

  • Scopus: 105049845074

Abstract

Background and objectives Cerebral Venous Thrombosis (CVT) generally has favorable outcomes, yet a subset develops disability despite anticoagulation. We externally validated three CVT prognostic tools: ISCVT-RS, CVT-GS, and IN-REvASC, for predicting a 6-month functional outcome. Methods This sub-study utilized data from CLOT-VENUS (CoLlabOraTion on Cerebral VENoUs Thrombosis Study), a multicenter registry including consecutive CVT cases from two tertiary stroke centers in the U.S. and Mexico (2004–2024). The primary outcome was poor functional status at 6 months (modified Rankin Scale (mRS) 3–6). Logistic regression models were built following each score’s original methodology. Discrimination was assessed using AUC and compared using DeLong tests. For predefined cut-offs, diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were assessed. Calibration and accuracy were evaluated with Hosmer-Lemeshow and Brier scores. Results Among 394 patients (median age 40 years; 65.5% female), 30.2% had intracerebral hemorrhage and 8.6% malignancy. All models demonstrated fair discrimination (AUC: ISCVT-RS 0.711; CVT-GS 0.737; IN-REvASC 0.764). For CVT-GS, a cutoff ≥ 8 prioritized specificity (0.970) with low sensitivity (0.214), whereas a cutoff ≥ 3 improved sensitivity (0.714) with acceptable specificity (0.722). IN-REvASC ≥ 20 showed high specificity (0.991) but poor sensitivity (0.022), while ≥ 10 provided a balanced sensitivity–specificity trade-off (0.489 and 0.829). Calibration was acceptable for CVT-GS risk score but suboptimal for ISCVT-RS and IN-REvASC. Conclusion In this binational CVT cohort, IN-REvASC achieved the highest discrimination, whereas CVT-GS offered the most favorable specificity–calibration balance. All three models would benefit from recalibration before clinical use.