Dataset for: Individualized Quantification of the Benefit from Reperfusion Therapy using Stroke Predictive Models
Brice Maxime Hugues Ozenne
Cho Tae-Hee
Irene Klaerke Mikkelsen
Marc Hermier
Götz Thomalla
Salvador Pedraza
Pascal Roy
Yves Berthezene
Norbert NIGHOGHOSSIAN
Leif Ostergaard
Jean-Claude Baron
Delphine Maucort-Boulch
10.6084/m9.figshare.8479274.v1
https://wiley.figshare.com/articles/dataset/Dataset_for_Individualized_Quantification_of_the_Benefit_from_Reperfusion_Therapy_using_Stroke_Predictive_Models/8479274
Purpose: Recent imaging developments have shown the potential of voxel-based models in assessing infarct growth after stroke. Many models have been proposed but their relevance in predicting the benefit of a reperfusion therapy remains unclear. We searched for a predictive model whose volumetric predictions would identify stroke patients who are to benefit from tissue Plasminogen Activator (t-PA)-induced reperfusion.
Material and Methods: Forty-five cases were used to study retrospectively stroke progression from admission to end of follow-up. Predictive approaches based on various statistical models, predictive variables, and spatial filtering methods were compared. The optimal approach was chosen according to the area under the precision-recall curve (AUPRC). The final lesion volume was then predicted assuming that the patient would or would not reperfuse. Patients with an acute lesion of ≤50 mL and a predicted reduction in presence of reperfusion >6 mL, and >25% of the acute lesion were classified as responders.
Results: The optimal model was a logistic regression using the voxel distance to the acute lesion, the volume of the acute lesion, and Gaussian-filtered MRI contrast parameters as predictive variables. The predictions gave a median AUPRC of 0.655, a median AUC of 0.976, and a median volumetric error of 8.29mL. Nineteen patients matched the responder profile. A non-significant trend of improved reduction in NIHSS score (-42.8%, p=0.09) and in lesion volume (-78.1%, p=0.21) following reperfusion was observed for responder patients.
Conclusion: Despite limited volumetric accuracy, predictive stroke models can be used to quantify the benefit of reperfusion therapies.
2019-07-31 14:36:41
Stroke
Magnetic resonance imaging
reperfusion
predictive modeling
Neuroscience