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Cerebrovasc Dis. 2009;28(1):39-44. doi: 10.1159/000215942. Epub 2009 May 6.

Risk score to predict the outcome of patients with cerebral vein and dural sinus thrombosis.

Author information

1
Department of Neurosciences-Neurology, Hospital Santa Maria, University of Lisbon, Lisbon, Portugal. jmferro@fm.ul.pt

Abstract

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 >or=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.

PMID:
19420921
DOI:
10.1159/000215942
[Indexed for MEDLINE]
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