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Stroke Diagnosis and Outcome Prediction
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Stroke Diagnosis and Outcome Prediction

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 2323

Special Issue Editor

Dr. Ramin Zand

E-Mail Website
Guest Editor
Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA, USA
Interests: stroke; cerebrovascular medicine; clinical informatics; atrial fibrillation; EHR; health disparity; neuroimaging; outcome prediction; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Stroke remains a significant global health concern, posing substantial challenges to healthcare systems worldwide. For the purpose of advancing our understanding and improving patient outcomes, we are pleased to announce a Special Issue dedicated to "Stroke Diagnosis and Outcome Prediction". This initiative aims to bring together cutting-edge research and innovative contributions that address the multifaceted aspects of stroke management. 

This Special Issue will encompass a broad spectrum of topics related to stroke diagnosis and outcome prediction, including but not limited to biomarkers and predictive models, technological innovations, clinical interventions and rehabilitation, and patient-centered approaches. Join us in this collaborative effort to deepen our understanding of stroke, enhance diagnostic capabilities, and ultimately improve the quality of life of individuals affected by this challenging medical condition.

Dr. Ramin Zand
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stroke
  • stroke management
  • biomarkers
  • predictive models
  • clinical interventions and rehabilitation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

16 pages, 2921 KiB  
Article
Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion
by Madhusmita Rout, April Vaughan, Evgeny V. Sidorov and Dharambir K. Sanghera
J. Clin. Med. 2024, 13(19), 5917; https://doi.org/10.3390/jcm13195917 - 3 Oct 2024
Viewed by 1154
Abstract
Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes [...] Read more.
Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes. Full article
(This article belongs to the Special Issue Stroke Diagnosis and Outcome Prediction)
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13 pages, 1557 KiB  
Article
Self-Management among Stroke Survivors in the United States, 2016 to 2021
by Ajith Kumar Vemuri, Seyyed Sina Hejazian, Alireza Vafaei Sadr, Shouhao Zhou, Keith Decker, Jonathan Hakun, Vida Abedi and Ramin Zand
J. Clin. Med. 2024, 13(15), 4338; https://doi.org/10.3390/jcm13154338 - 25 Jul 2024
Viewed by 854
Abstract
Background: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. Methods: We analyzed the Behavioral Risk [...] Read more.
Background: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. Methods: We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2021, a nationally representative health survey. A new outcome variable, stroke self-management (SSM = low or SSM = high), was defined based on five AHA guideline-recommended self-management practices, including regular physical activity, maintaining body mass index, regular doctor checkups, smoking cessation, and limiting alcohol consumption. A low level of self-management was defined as adherence to three or fewer practices. Results: Among 95,645 American stroke survivors, 46.7% have low self-management. Stroke survivors aged less than 65 are less likely to self-manage (low SSM: 56.8% vs. 42.3%; p < 0.0001). Blacks are less likely to self-manage than non-Hispanic Whites (low SSM: 52.0% vs. 48.6%; p < 0.0001); however, when adjusted for demographic and clinical factors, the difference was dissipated. Higher education and income levels are associated with better self-management (OR: 2.49, [95%CI: 2.16–2.88] and OR: 1.45, [95%CI: 1.26–1.67], respectively). Further sub-analysis revealed that women are less likely to be physically active (OR: 0.88, [95%CI: 0.81–0.95]) but more likely to manage their alcohol consumption (OR: 1.57, [95%CI: 1.29–1.92]). Stroke survivors residing in the Stroke Belt did not self-manage as well as their counterparts (low-SSM: 53.1% vs. 48.0%; p < 0.001). Conclusions: The substantial diversity in self-management practices emphasizes the need for tailored interventions. Particularly, multi-modal interventions should be targeted toward specific populations, including younger stroke survivors with lower education and income. Full article
(This article belongs to the Special Issue Stroke Diagnosis and Outcome Prediction)
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