Individualized quantification of the benefit from reperfusion therapy using stroke predictive models. (22nd July 2019)

• Record Type: Journal Article
• Authors: Ozenne, Brice; Cho, Tae‐Hee; Mikkelsen, Irene Klærke; Hermier, Marc; Thomalla, Götz; Pedraza, Salvador; Roy, Pascal; Berthezène, Yves; Nighoghossian, Norbert; Østergaard, Leif; Baron, Jean‐Claude; Maucort‐Boulch, Delphine
• Is Part Of: European journal of neuroscience. Volume 50:Number 8(2019)
• Page Start: 3251
• Page End: 3260
• Publication Date: 2019-07-22
• Subjects: magnetic resonance imaging -- predictive modelling -- reperfusion -- stroke; Nervous system -- Periodicals; 612.8
• DOI: 10.1111/ejn.14505
• Languages: English
• ISSNs: 0953-816X

Abstract

Abstract: 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 the 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 8.29 ml. Nineteen patients matched the responder profile. A non‐significant trend of improved reduction in NIHSS score (−42.8%, p = .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. Abstract : Predictive models can be used to obtain an individualized quantification of the benefit of reperfusion therapies for stroke. The predicted benefit can be used to define individualized eligibility criteria to reperfusion therapies. The set of predictors included in the model has a critical impact on the prediction accuracy.