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

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

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

Diagnostics ◽  
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.

Abstract 1122‐000106: Comparing Machine Learning Algorithms and Regression Models for Predicting Functional Outcome in the STRATIS Registry

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Mouhammad A Jumaa ◽  
Zeinab Zoghi ◽  
Syed Zaidi ◽  
Nils Mueller‐Kronast ◽  
Osama Zaidat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Systematic Evaluation ◽  
Machine Learning Model

Introduction : Machine learning algorithms have emerged as powerful predictive tools in the field of acute ischemic stroke. Here, we examine the predictive performance of a machine algorithm compared to logistic regression for predicting functional outcomes in the prospective Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry. Methods : The STRATIS Registry was a prospective, observational study of the use of the Solitaire device in acute ischemic stroke patients. Patients with posterior circulation stroke or missing 90‐day mRS were excluding from the analysis. A statistical algorithm (logistic regression) and a machine learning algorithm (decision tree) were implemented on the preprocessed dataset using 10‐fold cross‐validation method where 80% of the data were fed into the models to be trained and the remaining 20% were utilized in the test phase to evaluate the performance of the models for prediction of 90‐day mRS score as dichotomous output. Results : Of the 938 STRATIS patients, 702 with 90‐day mRS were included. The machine learning model outperformed the logistic regression model with a 0.92±0.026 Area Under Curve (AUC) score compared to a 0.88±0.028 AUC score obtained by implementing logistic regression. Conclusions : Our machine learning model delivered improved performance in comparison with the statistical model in predicting 90‐day functional outcome. More studies are needed to understand and externally validate the predictive capacity of our machine learning model.

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

PLoS ONE ◽  
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.

Is acute reperfusion therapy safe in acute ischemic stroke patients who harbor unruptured intracranial aneurysm?

2015 ◽  
Vol 10 (SA100) ◽  
pp. 113-118 ◽  
Author(s):  
Ashkan Mowla ◽  
Karanbir Singh ◽  
Sandhya Mehla ◽  
Mohammad K. Ahmed ◽  
Peyman Shirani ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Intracranial Aneurysm ◽  
Acute Ischemic Stroke ◽  
Reperfusion Therapy ◽  
Stroke Patients ◽  
Unruptured Intracranial Aneurysm

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

Stroke ◽  
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.

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

Stroke ◽  
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

scholarly journals Early NT-ProBNP (N-Terminal Probrain Natriuretic Peptide) Elevation Predicts Malignant Edema and Death After Reperfusion Therapy in Acute Ischemic Stroke Patients

Stroke ◽  
2021 ◽  
Author(s):  
Xuting Zhang ◽  
Shenqiang Yan ◽  
Wansi Zhong ◽  
Yannan Yu ◽  
Min Lou
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Natriuretic Peptide ◽  
Odds Ratio ◽  
Intravenous Thrombolysis ◽  
Reperfusion Therapy ◽  
Stroke Patients ◽  
Endovascular Thrombectomy ◽  
Median Serum ◽  
The Relationship

Background and Purpose: We aimed to investigate the relationship between early NT-proBNP (N-terminal probrain natriuretic peptide) and all-cause death in patients receiving reperfusion therapy, including intravenous thrombolysis and endovascular thrombectomy (EVT). Methods: This study included 1039 acute ischemic stroke patients with early NT-proBNP data at 2 hours after the beginning of alteplase infusion for those with intravenous thrombolysis only or immediately at the end of EVT for those with EVT. We performed natural log transformation for NT-proBNP (Ln(NT-proBNP)). Malignant brain edema was ascertained by using the SITS-MOST (Safe Implementation of Thrombolysis in Stroke-Monitoring Study) criteria. Results: Median serum NT-proBNP level was 349 pg/mL (interquartile range, 89–1250 pg/mL). One hundred twenty-one (11.6%) patients died. Malignant edema was observed in 78 (7.5%) patients. Ln(NT-proBNP) was independently associated with 3-month mortality in patients with intravenous thrombolysis only (odds ratio, 1.465 [95% CI, 1.169–1.836]; P =0.001) and in those receiving EVT (odds ratio, 1.563 [95% CI, 1.139–2.145]; P =0.006). The elevation of Ln(NT-proBNP) was also independently associated with malignant edema in patients with intravenous thrombolysis only (odds ratio, 1.334 [95% CI, 1.020–1.745]; P =0.036), and in those with EVT (odds ratio, 1.455 [95% CI, 1.057–2.003]; P =0.022). Conclusions: An early increase in NT-proBNP levels was related to malignant edema and stroke mortality after reperfusion therapy.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

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.

scholarly journals Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Biomedicines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1357
Author(s):  
Anthony Winder ◽  
Matthias Wilms ◽  
Jens Fiehler ◽  
Nils D. Forkert
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Treatment Group ◽  
Acute Ischemic Stroke ◽  
In Silico ◽  
Mechanical Thrombectomy ◽  
Intravenous Thrombolysis ◽  
Stroke Patients ◽  
New Devices

Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

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