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Individualized quantification of the benefit from reperfusion therapy using stroke predictive models.

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Title: Individualized quantification of the benefit from reperfusion therapy using stroke predictive models.
Authors: Ozenne, Brice (AUTHOR), Cho, Tae‐Hee (AUTHOR), Mikkelsen, Irene Klærke (AUTHOR), Hermier, Marc (AUTHOR), Thomalla, Götz (AUTHOR), Pedraza, Salvador (AUTHOR), Roy, Pascal (AUTHOR), Berthezène, Yves (AUTHOR), Nighoghossian, Norbert (AUTHOR), Østergaard, Leif (AUTHOR), Baron, Jean‐Claude (AUTHOR), Maucort‐Boulch, Delphine (AUTHOR)
Source: European Journal of Neuroscience. Oct2019, Vol. 50 Issue 8, p3251-3260. 10p. 2 Diagrams, 3 Charts, 1 Graph.
Subjects: Prediction models, Tissue plasminogen activator, Reperfusion, Stroke, Spatial filters
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 FROM AUTHOR]
Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Individualized quantification of the benefit from reperfusion therapy using stroke predictive models.
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  Data: <searchLink fieldCode="AR" term="%22Ozenne%2C+Brice%22">Ozenne, Brice</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cho%2C+Tae‐Hee%22">Cho, Tae‐Hee</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mikkelsen%2C+Irene+Klærke%22">Mikkelsen, Irene Klærke</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hermier%2C+Marc%22">Hermier, Marc</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thomalla%2C+Götz%22">Thomalla, Götz</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pedraza%2C+Salvador%22">Pedraza, Salvador</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roy%2C+Pascal%22">Roy, Pascal</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Berthezène%2C+Yves%22">Berthezène, Yves</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nighoghossian%2C+Norbert%22">Nighoghossian, Norbert</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Østergaard%2C+Leif%22">Østergaard, Leif</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Baron%2C+Jean‐Claude%22">Baron, Jean‐Claude</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Maucort‐Boulch%2C+Delphine%22">Maucort‐Boulch, Delphine</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neuroscience%22">European Journal of Neuroscience</searchLink>. Oct2019, Vol. 50 Issue 8, p3251-3260. 10p. 2 Diagrams, 3 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Tissue+plasminogen+activator%22">Tissue plasminogen activator</searchLink><br /><searchLink fieldCode="DE" term="%22Reperfusion%22">Reperfusion</searchLink><br /><searchLink fieldCode="DE" term="%22Stroke%22">Stroke</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+filters%22">Spatial filters</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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 FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/ejn.14505
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        Text: English
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        Type: general
      – SubjectFull: Tissue plasminogen activator
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      – SubjectFull: Reperfusion
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      – SubjectFull: Stroke
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      – SubjectFull: Spatial filters
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              Text: Oct2019
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