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Effective reserve: a latent variable to improve outcome prediction in stroke

View ORCID ProfileMarkus D. Schirmer, Mark R. Etherton, Adrian V. Dalca, Anne-Katrin Giese, Lisa Cloonan, Ona Wu, Polina Golland, Natalia S. Rost
doi: https://doi.org/10.1101/192823
Markus D. Schirmer
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
2Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, USA
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  • ORCID record for Markus D. Schirmer
Mark R. Etherton
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
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Adrian V. Dalca
2Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, USA
3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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Anne-Katrin Giese
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge MA, USA
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Lisa Cloonan
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
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Ona Wu
3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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Polina Golland
2Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, USA
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Natalia S. Rost
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
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Abstract

Background Prediction of functional outcome after stroke based on initial presentation remains an open challenge, suggesting that an important aspect is missing from these prediction models. There exists the notion of a protective mechanism called brain reserve, which may be utilized to understand variations in disease outcome. In this work we expand the concept of brain reserve (effective reserve) to improve prediction models of functional outcome after acute ischemic stroke (AIS).

Methods Consecutive AIS patients with acute brain MRI (<48 hours) were eligible for this study. White matter hyperintensity and acute infarct volume were determined on T2 fluid attenuated inversion recovery and diffusion weighted images, respectively. Modified Rankin Scale scores (mRS) were obtained at 90 days post-stroke. Effective reserve (eR) was defined as a latent variable using structural equation modeling by including age, systolic blood pressure, and intracranial volume measurements.

Results Of 453 AIS patients (mean age 66.6±14.7 years), 36% were male and 311 hypertensive. There was inverse association between eR and 90-day mRS (path coefficient -0.18±0.01, p<0.01). As compared to a model without eR, correlation between predicted mRS and observed mRS improved in the eR-based model (Spearman’s ρ 0.29±0.18 versus 0.15±0.17, p<0.001). Furthermore, hypertensive patients exhibited lower eR (p<10−6).

Conclusion Using eR in prediction models of stroke outcome is feasible and leads to better model performance. Furthermore, higher eR is associated with more favorable functional post-stoke outcome and might correspond to an overall better vascular health.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 23, 2017.
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Effective reserve: a latent variable to improve outcome prediction in stroke
Markus D. Schirmer, Mark R. Etherton, Adrian V. Dalca, Anne-Katrin Giese, Lisa Cloonan, Ona Wu, Polina Golland, Natalia S. Rost
bioRxiv 192823; doi: https://doi.org/10.1101/192823
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Effective reserve: a latent variable to improve outcome prediction in stroke
Markus D. Schirmer, Mark R. Etherton, Adrian V. Dalca, Anne-Katrin Giese, Lisa Cloonan, Ona Wu, Polina Golland, Natalia S. Rost
bioRxiv 192823; doi: https://doi.org/10.1101/192823

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