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Validation of DRAGON Versus a Simplified DRAGON/Machine Learning

2019-09-23 04:51:45 | BioPortfolio

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.

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

Conditions

Stroke, Acute

Location

Ziekenhuis Oost-Limburg
Genk
Belgium
3600

Status

Recruiting

Source

Ziekenhuis Oost-Limburg

Results (where available)

View Results

Links

Published on BioPortfolio: 2019-09-23T04:51:45-0400

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