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The CT-DRAGON score can predict long-term functional outcome after acute stroke treated by thrombolysis. However, implementation in clinical practice is hampered by a lack of validation in the broad spectrum of stroke patients undergoing thrombectomy, whether or not in combination with thrombolysis or conservative treatment. Furthermore, the CT-DRAGON score considers multiple items, which are not always readily available in every setting. This study aims to investigate whether either a simplified version of the CT-DRAGON score with only three clinical items or a machine learning technique could be as powerful and more feasible.
The investigators aim to validate the CT-DRAGON score in all ischaemic stroke localisations and for all treatment options, including a conservative treatment policy. The predictability will then be compared with on the one hand simplified prognostic models that include only a selective set of highly predictive parameters that have already been described in the literature, such as patient age, National Institutes of Health Stroke Scale (NIHSS) and pre-stroke modified Rankin Scale (mRS). On the other hand, machine learning techniques, that incorporate a large set of variables and have recently shown some promising results, will also be applied to predict long-term outcome after ischaemic stroke.
Published on BioPortfolio: 2019-09-23T04:51:45-0400
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A morpholine and thiophene derivative that functions as a FACTOR XA INHIBITOR and is used in the treatment and prevention of DEEP-VEIN THROMBOSIS and PULMONARY EMBOLISM. It is also used for the prevention of STROKE and systemic embolization in patients with non-valvular ATRIAL FIBRILLATION, and for the prevention of atherothrombotic events in patients after an ACUTE CORONARY SYNDROME.
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