Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/32916
Title: Prediction of Functional Outcome After Acute Ischemic Stroke: Comparison of the CT-DRAGON Score and a Reduced Features Set
Authors: Lesenne, Anouk
Grieten, Jef
Ernon, Ludovic
Wibail, Alain
STOCKX, Luc 
Wouters, Patrick F.
Vundelinckx, Joris
Vandermeulen, Elly
VAN BOXSTAEL, Sam 
VANELDEREN, Pascal 
VAN POUCKE, Sven 
Dreesen, Leentje
VAN CAUTER, Sofie 
MESOTTEN, Dieter 
Issue Date: 2020
Publisher: FRONTIERS MEDIA SA
Source: FRONTIERS IN NEUROLOGY, 11 (Art N° 718)
Abstract: Background and Purpose:The CT-DRAGON score was developed to predict long-term functional outcome after acute stroke in the anterior circulation treated by thrombolysis. Its implementation in clinical practice may be hampered by its plethora of variables. The current study was designed to develop and evaluate an alternative score, as a reduced set of features, derived from the original CT-DRAGON score. Methods:This single-center retrospective study included 564 patients treated for stroke, in the anterior and the posterior circulation. At 90 days, favorable [modified Rankin Scale score (mRS) of 0-2] and miserable outcome (mRS of 5-6) were predicted by the CT-DRAGON in 427 patients. Bootstrap forests selected the most relevant parameters of the CT-DRAGON, in order to develop a reduced set of features. Discrimination, calibration and misclassification of both models were tested. Results:The area under the receiver operating characteristic curve (AUROC) for the CT-DRAGON was 0.78 (95% CI 0.74-0.81) for favorable and 0.78 (95% CI 0.72-0.83) for miserable outcome. Misclassification was 29% for favorable and 13.5% for miserable outcome, with a 100% specificity for the latter. National Institutes of Health Stroke Scale (NIHSS), pre-stroke mRS and age were identified as the strongest contributors to favorable and miserable outcome and named the reduced features set. While CT-DRAGON was only available in 323 patients (57%), the reduced features set could be calculated in 515 patients (91%) (p < 0.001). Misclassification was 25.8% for favorable and 14.4% for miserable outcome, with a 97% specificity for miserable outcome. The reduced features set had better discriminative power than CT-DRAGON for both outcomes (both p < 0.005), with an AUROC of 0.82 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.87) for favorable and miserable outcome, respectively. Conclusions:The CT-DRAGON score revealed acceptable discrimination in our cohort of both anterior and posterior circulation strokes, receiving all treatment modalities. The reduced features set could be measured in a larger cohort and with better discrimination. However, the reduced features set needs further validation in a prospective, multicentre study.
Notes: Mesotten, D (corresponding author), Ziekenhuis Oost Limburg Genk, Dept Crit Care Serv, Genk, Belgium.; Mesotten, D (corresponding author), UHasselt, Fac Med & Life Sci, Diepenbeek, Belgium.
dieter.mesotten@zol.be
Other: Mesotten, D (corresponding author), Ziekenhuis Oost Limburg Genk, Dept Crit Care Serv, Genk, Belgium ; UHasselt, Fac Med & Life Sci, Diepenbeek, Belgium. dieter.mesotten@zol.be
Keywords: cerebrovascular disorders;stroke;prognosis;machine learning;thrombectomy;thrombolytic therapy
Document URI: http://hdl.handle.net/1942/32916
ISSN: 1664-2295
e-ISSN: 1664-2295
DOI: 10.3389/fneur.2020.00718
ISI #: WOS:000561661900001
Rights: Open Access.
Category: A1
Type: Journal Contribution
Validations: ecoom 2021
Appears in Collections:Research publications

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