Risk Score to Predict the Outcome of Patients with Cerebral Vein and Dural Sinus ThrombosisFerro J.M.a · Bacelar-Nicolau H.b · Rodrigues T.b · Bacelar-Nicolau L.b · Canhão P.a · Crassard I.d · Bousser M.-G.d · Dutra A.P.f · Massaro A.f · Mackowiack-Cordiolani M.-A.e · Leys D.e · Fontes J.c · Stam J.g · Barinagarrementeria F.h
aDepartment of Neurosciences (Neurology), Hospital Santa Maria, University of Lisbon, bLaboratory of Biomathematics, Faculdade de Medicina de Lisboa, Lisbon, and cDepartment of Neurology, Hospital de São Marcos, Braga, Portugal; dDepartment of Neurology, Hôpital Lariboisière, Paris, and eDepartment of Neurology, University Hospital, Lille, France; fDepartment of Neurology, Hospital das Clínicas, Universidade de São Paulo, São Paulo, Brazil; gDepartment of Neurology, Academic Medical Centre, Amsterdam, The Netherlands; hDepartment of Neurology, Instituto Nacional de Neurologia y Neurocirurgia, Mexico City, Mexico
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Article / Publication Details
Background: Around 15% of patients die or become dependent after cerebral vein and dural sinus thrombosis (CVT). Method: We used the International Study on Cerebral Vein and Dural Sinus Thrombosis (ISCVT) sample (624 patients, with a median follow-up time of 478 days) to develop a Cox proportional hazards regression model to predict outcome, dichotomised by a modified Rankin Scale score >2. From the model hazard ratios, a risk score was derived and a cut-off point selected. The model and the score were tested in 2 validation samples: (1) the prospective Cerebral Venous Thrombosis Portuguese Collaborative Study Group (VENOPORT) sample with 91 patients; (2) a sample of 169 consecutive CVT patients admitted to 5 ISCVT centres after the end of the ISCVT recruitment period. Sensitivity, specificity, c statistics and overall efficiency to predict outcome at 6 months were calculated. Results: The model (hazard ratios: malignancy 4.53; coma 4.19; thrombosis of the deep venous system 3.03; mental status disturbance 2.18; male gender 1.60; intracranial haemorrhage 1.42) had overall efficiencies of 85.1, 84.4 and 90.0%, in the derivation sample and validation samples 1 and 2, respectively. Using the risk score (range from 0 to 9) with a cut-off of ≥3 points, overall efficiency was 85.4, 84.4 and 90.1% in the derivation sample and validation samples 1 and 2, respectively. Sensitivity and specificity in the combined samples were 96.1 and 13.6%, respectively. Conclusions: The CVT risk score has a good estimated overall rate of correct classifications in both validation samples, but its specificity is low. It can be used to avoid unnecessary or dangerous interventions in low-risk patients, and may help to identify high-risk CVT patients.
© 2009 S. Karger AG, Basel
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