Status
Conditions
About
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.
Full description
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.
Enrollment
Sex
Volunteers
Inclusion criteria
Exclusion criteria
700 participants in 1 patient group
Loading...
Central trial contact
Elly Vandermeulen, PhD; Dieter Mesotten, MD PhD
Data sourced from clinicaltrials.gov
Clinical trials
Research sites
Resources
Legal