Nashwa Abdelhakim, Milagros Galecio-Castillo, Leonardo Cruz-Criollo, Anderson Brito, Aaron Rodriguez-Calienes, Eric Kontowicz, Amir Shaban, Vanessa Cano Nigenda, Andres Alberto Mercado Pompa, Adrian Pereda-Castillo, Hector Valdez Ruvalcaba, Brian J Smith, Miguel A Barboza, Antonio Arauz, Santiago Ortega-Gutierrez
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.
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.