Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

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Using multiclass machine learning model to improve outcome prediction of acute ischemic stroke patients after reperfusion therapy

2020 International Computer Symposium (ICS) ◽

10.1109/ics51289.2020.00053 ◽

2020 ◽

Author(s):

I-Min Chiu ◽

Wun-Huei Zeng ◽

Chun-Hung Richard Lin

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Reperfusion Therapy ◽

Learning Model ◽

Stroke Patients ◽

Improve Outcome ◽

Machine Learning Model

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Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy

Diagnostics ◽

10.3390/diagnostics11010080 ◽

2021 ◽

Vol 11 (1) ◽

pp. 80

Author(s):

I-Min Chiu ◽

Wun-Huei Zeng ◽

Chi-Yung Cheng ◽

Shih-Hsuan Chen ◽

Chun-Hung Richard Lin

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Reperfusion Therapy ◽

Gradient Boosting ◽

Stroke Patients ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5–6, should be considered as well before further invasive intervention. By using a machine learning algorithm, we aimed to develop a multiclass classification model for outcome prediction in acute ischemic stroke patients requiring reperfusion therapy. This was a retrospective study performed at a stroke medical center in Taiwan. Patients with acute ischemic stroke who visited between January 2016 and December 2019 and who were candidates for reperfusion therapy were included. Clinical outcomes were classified as favorable outcome, intermediate outcome, and miserable outcome. We developed four different multiclass machine learning models (Logistic Regression, Supportive Vector Machine, Random Forest, and Extreme Gradient Boosting) to predict clinical outcomes and compared their performance to the DRAGON score. A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression: 0.70, Supportive Vector Machine: 0.67, Random Forest: 0.69, and Extreme Gradient Boosting: 0.67, vs. DRAGON: 0.51, p < 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specificity. Compared with the DRAGON score, the multiclass machine learning approach showed better performance on the prediction of the 3-month functional outcome of acute ischemic stroke patients requiring reperfusion therapy.

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Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

PLoS ONE ◽

10.1371/journal.pone.0241917 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0241917

Author(s):

Malte Grosser ◽

Susanne Gellißen ◽

Patrick Borchert ◽

Jan Sedlacik ◽

Jawed Nawabi ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Hybrid Approach ◽

Learning Models ◽

Stroke Patients ◽

Local Approach ◽

Prediction Approach ◽

Machine Learning Models

Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

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Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning

Journal of Stroke and Cerebrovascular Diseases ◽

10.1016/j.jstrokecerebrovasdis.2021.105742 ◽

2021 ◽

Vol 30 (6) ◽

pp. 105742

Author(s):

Mi-Yeon Eun ◽

Eun-Tae Jeon ◽

Kwon-Duk Seo ◽

Dongwhane Lee ◽

Jin-Man Jung

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Meta Analysis ◽

Reperfusion Therapy ◽

Active Cancer

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Abstract TP215: Machine Learning Analysis of Gene Expression Data and Predictive Stroke Outcomes in Acute Ischemic Stroke

Stroke ◽

10.1161/str.51.suppl_1.tp215 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Sarah R Martha ◽

Qiang Cheng ◽

Liyu Gong ◽

Lisa Collier ◽

Stephanie Davis ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Mechanical Thrombectomy ◽

Stroke Patients ◽

Stroke Outcomes ◽

Inflammatory Genes ◽

Patient Demographics ◽

Edema Volume

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.

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