Cargando…

Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke

Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothe...

Descripción completa

Detalles Bibliográficos
Autores principales: Horn, John D., Johnson, Michael J., Starosolski, Zbigniew, Meoded, Avner, Milewicz, Dianna M., Annapragada, Ananth, Hossain, Shaolie S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920501/
https://www.ncbi.nlm.nih.gov/pubmed/35295566
http://dx.doi.org/10.3389/fphys.2022.846404
_version_ 1784669140704821248
author Horn, John D.
Johnson, Michael J.
Starosolski, Zbigniew
Meoded, Avner
Milewicz, Dianna M.
Annapragada, Ananth
Hossain, Shaolie S.
author_facet Horn, John D.
Johnson, Michael J.
Starosolski, Zbigniew
Meoded, Avner
Milewicz, Dianna M.
Annapragada, Ananth
Hossain, Shaolie S.
author_sort Horn, John D.
collection PubMed
description Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothesized that local hemodynamics are predictive of future stroke and set out to noninvasively assess this stroke risk in pediatric MMD patients. MR and X-ray angiography imaging were utilized to reconstruct patient-specific models of the circle of Willis of six pediatric MMD patients who had previous strokes, along with a control subject. Blood flow simulations were performed by using a Navier–Stokes solver within an isogeometric analysis framework. Vascular regions with a wall shear rate (WSR) above the coagulation limit (>5,000 s(−1)) were identified to have a higher probability of thrombus formation, potentially leading to ischemic stroke(s). Two metrics, namely, “critical WSR coverage” and “WSR score,” were derived to assess contralateral stroke risk and compared with clinical follow-up data. In two patients that suffered a contralateral stroke within 2 months of the primary stroke, critical WSR coverages exceeding 50% of vessel surface and WSR scores greater than 6× the control were present in multiple contralateral vessels. These metrics were not as clearly indicative of stroke in two additional patients with 3–5 year gaps between primary and contralateral strokes. However, a longitudinal study of one of these two cases, where a subsequent timepoint was analyzed, suggested disease stabilization on the primary stroke side and an elevated contralateral stroke risk, which was confirmed by patient outcome data. This indicates that post-stroke follow-up at regular intervals might be warranted for secondary stroke prevention. The findings of this study suggest that WSR-based metrics could be predictive of future stroke risk after an initial stroke in pediatric MMD patients. In addition, better predictions may be possible by performing patient-specific hemodynamic analysis at multiple timepoints during patient follow-up to monitor changes in the WSR-based metrics.
format Online
Article
Text
id pubmed-8920501
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89205012022-03-15 Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke Horn, John D. Johnson, Michael J. Starosolski, Zbigniew Meoded, Avner Milewicz, Dianna M. Annapragada, Ananth Hossain, Shaolie S. Front Physiol Physiology Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothesized that local hemodynamics are predictive of future stroke and set out to noninvasively assess this stroke risk in pediatric MMD patients. MR and X-ray angiography imaging were utilized to reconstruct patient-specific models of the circle of Willis of six pediatric MMD patients who had previous strokes, along with a control subject. Blood flow simulations were performed by using a Navier–Stokes solver within an isogeometric analysis framework. Vascular regions with a wall shear rate (WSR) above the coagulation limit (>5,000 s(−1)) were identified to have a higher probability of thrombus formation, potentially leading to ischemic stroke(s). Two metrics, namely, “critical WSR coverage” and “WSR score,” were derived to assess contralateral stroke risk and compared with clinical follow-up data. In two patients that suffered a contralateral stroke within 2 months of the primary stroke, critical WSR coverages exceeding 50% of vessel surface and WSR scores greater than 6× the control were present in multiple contralateral vessels. These metrics were not as clearly indicative of stroke in two additional patients with 3–5 year gaps between primary and contralateral strokes. However, a longitudinal study of one of these two cases, where a subsequent timepoint was analyzed, suggested disease stabilization on the primary stroke side and an elevated contralateral stroke risk, which was confirmed by patient outcome data. This indicates that post-stroke follow-up at regular intervals might be warranted for secondary stroke prevention. The findings of this study suggest that WSR-based metrics could be predictive of future stroke risk after an initial stroke in pediatric MMD patients. In addition, better predictions may be possible by performing patient-specific hemodynamic analysis at multiple timepoints during patient follow-up to monitor changes in the WSR-based metrics. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8920501/ /pubmed/35295566 http://dx.doi.org/10.3389/fphys.2022.846404 Text en Copyright © 2022 Horn, Johnson, Starosolski, Meoded, Milewicz, Annapragada and Hossain. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Horn, John D.
Johnson, Michael J.
Starosolski, Zbigniew
Meoded, Avner
Milewicz, Dianna M.
Annapragada, Ananth
Hossain, Shaolie S.
Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title_full Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title_fullStr Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title_full_unstemmed Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title_short Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke
title_sort patient-specific modeling could predict occurrence of pediatric stroke
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920501/
https://www.ncbi.nlm.nih.gov/pubmed/35295566
http://dx.doi.org/10.3389/fphys.2022.846404
work_keys_str_mv AT hornjohnd patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT johnsonmichaelj patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT starosolskizbigniew patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT meodedavner patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT milewiczdiannam patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT annapragadaananth patientspecificmodelingcouldpredictoccurrenceofpediatricstroke
AT hossainshaolies patientspecificmodelingcouldpredictoccurrenceofpediatricstroke