Validation of DRAGON Versus a Simplified DRAGON/Machine Learning

Status Recruiting

Clinical Trial Summary

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.

Clinical Trial 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. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT04092543
Study type	Observational
Source	Ziekenhuis Oost-Limburg
Contact	Dieter Mesotten, MD PhD
Phone	+3289325407
Email	elly.vandermeulen@zol.be
Status	Recruiting
Phase
Start date	March 1, 2019
Completion date	November 1, 2021

View Details

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