Neurologic Outcomes of Carotid and Other Emergent Interventions for Ischemic Stroke Over 6 Years With Analysis Enhanced by Machine Learning

Background and Purpose: The ability to predict ischemic stroke outcomes in the first day of admission could be vital for patient counseling, rehabilitation, and care planning. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) collects blood samples distal and proximal to the intracranial thrombus during mechanical thrombectomy. These samples are a novel resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and patient demographics that are predictive of stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability, convenience sampling of subjects (≥ 18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion. We evaluated relative concentrations of mRNA for gene expression in 84 inflammatory molecules in static blood distal and proximal to the intracranial thrombus from adults who underwent thrombectomy. We employed a machine learning method, Random Forest, utilizing the first set of enrolled subjects, to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. Results: We analyzed the first 28 subjects (age = 66 ± 15.48, 11 males) in the BACTRAC registry. Results from machine learning analyses demonstrate that the genes CCR4, IFNA2, IL9, CXCL3, Age, DM, IL7, CCL4, BMI, IL5, CCR3, TNF, and IL27 predict infarct volume. The genes IFNA2, IL5, CCL11, IL17C, CCR4, IL9, IL7, CCR3, IL27, DM, and CSF2 predict edema volume. There is an intersection of genes CCR4, IFNA2, IL9, IL7, IL5, CCR3 to both infarct and edema volumes. Overall, these genes depicts a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop predictive biomarker signatures for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

Download Full-text

Abstract WMP23: Automated ASPECTS Scoring of CT Scans for Acute Ischemic Stroke Patients Using Machine Learning

Stroke ◽

10.1161/str.49.suppl_1.wmp23 ◽

2018 ◽

Vol 49 (Suppl_1) ◽

Author(s):

Hulin Kuang ◽

Ericka Teleg ◽

Mohamed Najm ◽

Alexis T Wilson ◽

Sung I Sohn ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Ct Scans ◽

Stroke Patients

Download Full-text

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy—prospective diagnostic accuracy study in ischemic stroke patients

Neuroradiology ◽

10.1007/s00234-020-02419-7 ◽

2020 ◽

Vol 62 (10) ◽

pp. 1239-1245

Author(s):

Jiri Kral ◽

Martin Cabal ◽

Linda Kasickova ◽

Jaroslav Havelka ◽

Tomas Jonszta ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Diagnostic Accuracy ◽

Brain Lesions ◽

Diagnostic Accuracy Study ◽

Stroke Patients ◽

Ischemic Brain ◽

Accuracy Study

Download Full-text

Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2018.2811471 ◽

2018 ◽

Vol 15 (6) ◽

pp. 1953-1959 ◽

Cited By ~ 13

Author(s):

Miguel Monteiro ◽

Ana Catarina Fonseca ◽

Ana Teresa Freitas ◽

Teresa Pinho e Melo ◽

Alexandre P. Francisco ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Functional Outcome ◽

Stroke Patients

Download Full-text

Socioeconomic determinants of outcome after childhood arterial ischemic stroke

Neurology ◽

10.1212/wnl.0000000000005946 ◽

2018 ◽

Vol 91 (6) ◽

pp. e509-e516 ◽

Cited By ~ 4

Author(s):

Lori C. Jordan ◽

Nancy K. Hills ◽

Christine K. Fox ◽

Rebecca N. Ichord ◽

Paola Pergami ◽

...

Keyword(s):

Ischemic Stroke ◽

Low Income ◽

Maternal Education ◽

Univariate Analysis ◽

Multivariable Analysis ◽

Rehabilitation Services ◽

Pediatric Stroke ◽

Arterial Ischemic Stroke ◽

Annual Household Income ◽

Neurologic Outcomes

ObjectiveTo determine whether lower socioeconomic status (SES) is associated with worse 1-year neurologic outcomes and reduced access to rehabilitation services in children with arterial ischemic stroke (AIS).MethodsFrom 2010 to 2014, the Vascular effects of Infection in Pediatric Stroke (VIPS) observational study prospectively enrolled and confirmed 355 children (age 29 days–18 years) with AIS at 37 international centers. SES markers measured via parental interview included annual household income (US dollars) at the time of enrollment, maternal education level, and rural/suburban/urban residence. Receipt of rehabilitation services was measured by parental report. Pediatric Stroke Outcome Measure scores were categorized as 0 to 1, 1.5 to 3, 3.5 to 6, and 6.5 to 10. Univariate and multivariable ordinal logistic regression models examined potential predictors of outcome.ResultsAt 12 ± 3 months after stroke, 320 children had documented outcome measurements, including 15 who had died. In univariate analysis, very low income (<US $10,000), but not other markers of SES, was associated with worse outcomes (odds ratio [OR] 3.13, 95% confidence interval [CI] 1.43–6.88, p = 0.004). In multivariable analysis, including adjustment for stroke etiology, this association persisted (OR 3.17, 95% CI 1.18–8.47, p = 0.02). Income did not correlate with receiving rehabilitation services at 1 year after stroke; however, quality and quantity of services were not assessed.ConclusionsIn a large, multinational, prospective cohort of children with AIS, low income was associated with worse neurologic outcomes compared to higher income levels. This difference was not explained by stroke type, neurologic comorbidities, or reported use of rehabilitation services. The root causes of this disparity are not clear and warrant further investigation.

Download Full-text

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

PLoS ONE ◽

10.1371/journal.pone.0225841 ◽

2019 ◽

Vol 14 (12) ◽

pp. e0225841 ◽

Cited By ~ 3

Author(s):

Seán Fitzgerald ◽

Shunli Wang ◽

Daying Dai ◽

Dennis H. Murphree ◽

Abhay Pandit ◽

...

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Blood Clots ◽

Learning Software

Download Full-text

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2019-015135 ◽

2019 ◽

Vol 12 (2) ◽

pp. 156-164 ◽

Cited By ~ 15

Author(s):

Nick M Murray ◽

Mathias Unberath ◽

Gregory D Hager ◽

Ferdinand K Hui

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Systematic Review ◽

Ischemic Stroke ◽

Acute Stroke ◽

Feature Detection ◽

Performance Metrics ◽

Large Vessel ◽

Image Feature ◽

Stroke Treatment

Background and purposeAcute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.MethodsA systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.ResultsVariations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).ConclusionsAI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

Download Full-text