Abstract P506: Machine Learning Models Improve Prediction of Large Vessel Occlusion and Mechanical Thrombectomy Candidacy in Acute Ischemic Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Shon Thomas ◽  
Paula De La Pena ◽  
Liam Butler ◽  
Oguz Akbilgic ◽  
Daniel Heiferman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Random Forest ◽  
Acute Ischemic Stroke ◽  
Predictive Value ◽  
Mechanical Thrombectomy ◽  
Demographic Variables ◽  
Large Vessel ◽  
Stroke Patients ◽  
Forest Model

Background and Purpose: Early identification of large vessel occlusions (LVO) and timely recanalization are paramount in improved clinical outcomes in acute ischemic stroke. Multiple simple stroke scales have good sensitivity, but compromise specificity for predicting LVO. No scale has been shown to predict mechanical thrombectomy (MT) candidacy. Machine learning techniques are being used for predictive modeling in many aspects of stroke care and may have potential in predicting LVO presence and MT candidacy. Methods: 287 acute ischemic stroke patients from July 2018 to July 2019 at Loyola University Medical Center were included. 36 clinical and demographic variables were analyzed using machine learning and statistical algorithms, including logistic regression, extreme gradient boosting, random forest, and decision trees to build models predictive of LVO and MT. The best performing model was compared with prior stroke scales. Results: Random forest based model resulted in the highest classification performance to predict both LVO and MT outcomes with an area under the curve (AUC) of 0.90±0.07 and 0.94±0.04, respectively. When the predictors were reduced to 7, random forest maintained a high AUC for predicting LVO (0.89). When reduced to 10 predictors, the random forest model predicted MT with an AUC = 0.93. Random forest models had excellent sensitivity and specificity of 0.86 and 0.89 for LVO and 0.89 and 0.95 for MT, respectively. The negative predictive value was 0.94 for LVO and 0.98 for MT while the positive predictive value was 0.77 for LVO and 0.79 for MT. With equal sensitivity, the random forest model was favorable to all previous stroke scales. Conclusion: Machine learning utilizing clinical and demographic variables predicts LVO and patient candidacy for MT with a high degree of accuracy. Further validation of this strategy for triage of stroke patients requires prospective and external validation.

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 WP372: Hydration Status and Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Mechanical Thrombectomy

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Mona N Bahouth ◽  
Rebecca Gottesman
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Clinical Outcomes ◽  
Blood Urea Nitrogen ◽  
Mechanical Thrombectomy ◽  
Large Vessel ◽  
Hydration Status ◽  
Creatinine Ratio ◽  
Stroke Patients ◽  
Anterior Circulation

Introduction: Impaired hydration measured by elevated blood urea nitrogen (BUN) to creatinine ratio has been associated with worsened outcome after acute ischemic stroke. Whether hydration status is relevant for patients with acute ischemic stroke treated with mechanical thrombectomy remains unknown. Materials and Methods: We conducted a retrospective review of consecutive acute ischemic stroke patients who underwent endovascular procedures for anterior circulation large artery occlusion at Johns Hopkins Comprehensive Stroke Centers between 2012 and 2017. A volume contracted state (VCS), was determined based on surrogate lab markers and defined as blood urea nitrogen (BUN) to creatinine ratio greater than 15. Endpoints were achievement of successful revascularization (TICI 2b or 3), early re-occlusion, and short term clinical outcomes including development of early neurological worsening and functional outcome at 3 months. Results: Of the 158 patients who underwent an endovascular procedure, 102 patients had a final diagnosis of anterior circulation large vessel occlusion and met the inclusion criteria for analysis. Volume contracted state was present in 62/102 (61%) of patients. Successful revascularization was achieved in 75/102 (74%) of the cohort. There was no relationship between VCS and successful revascularization, but there was a 1.13 increased adjusted odds (95% CI 1.01, 1.27) of re-occlusion within 24 hours for every point higher BUN/creatinine ratio in the subset of patients who underwent radiological testing for pre-procedure planning (n=57). There was no relationship between VCS and clinical outcomes including early neurological worsening and 3 month outcome. Conclusions: Patients with VCS and large vessel anterior circulation stroke may have a higher odds of early re-occlusion after mechanical thrombectomy than their non-VCS counterparts, but no differences in successful revascularization nor clinical outcomes were present in this cohort. These results may suggest an opportunity for the exploration of pre-procedure hydration to improve outcomes.

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.

scholarly journals Mechanical thrombectomy techniques for acute ischemic stroke

2020 ◽  
pp. 46-51
Author(s):  
A. Chiriac ◽  
Georgiana Ion ◽  
N. Dobrin ◽  
Dana Turliuc ◽  
I. Poeata
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Mechanical Thrombectomy ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Stroke Patients ◽  
Vessel Occlusion ◽  
Current Trends

Mechanical thrombectomy technique was introduced as an effective and secure method in acute ischemic stroke patients suffering from intracranial large vessel occlusion (LVO). In this article, we will review the main mechanical thrombectomy techniques and current trends in this type of treatment for acute ischemic stroke.

scholarly journals Mortality Risk in Acute Ischemic Stroke Patients With Large Vessel Occlusion Treated With Mechanical Thrombectomy

2019 ◽  
Vol 8 (21) ◽  
Author(s):  
Aristeidis H. Katsanos ◽  
Konark Malhotra ◽  
Nitin Goyal ◽  
Lina Palaiodimou ◽  
Peter D. Schellinger ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Mortality Risk ◽  
Mechanical Thrombectomy ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Stroke Patients ◽  
Vessel Occlusion

Rationale and design of combination of an immune modulator Fingolimod with Alteplase bridging with Mechanical Thrombectomy in Acute Ischemic Stroke (FAMTAIS) trial

2017 ◽  
Vol 12 (8) ◽  
pp. 906-909 ◽  
Author(s):  
Sheng Zhang ◽  
Ying Zhou ◽  
Ruiting Zhang ◽  
Meixia Zhang ◽  
Bruce Campbell ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Reperfusion Injury ◽  
Mechanical Thrombectomy ◽  
Disease Onset ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Stroke Patients ◽  
Bridging Therapy ◽  
Vessel Occlusion

Rationale In acute ischemic stroke patients with large vessel occlusion, although reperfusion within 6 h after stroke onset using combined intravenous alteplase and mechanical thrombectomy (bridging therapy) can improve functional outcome, still approximately 50% patients suffer disability which may result from reperfusion injury. Proof-of-concept clinical trials have indicated that the sphingosine-1-phosphate receptor modulator fingolimod may be efficacious in attenuating brain inflammation and improving clinical outcomes in acute ischemic stroke patients as a single therapy beyond 4.5 h of disease onset, or in combination with alteplase within 4.5 h of disease onset. Aim To assess whether the treatment of fingolimod combined with bridging therapy in large vessel occlusion acute ischemic stroke patients is effective and safe. Design and sample size estimates Fingolimod with Alteplase bridging with Mechanical Thrombectomy in Acute Ischemic Stroke (FAMTAIS) study is a randomized, open-label, multiple central trial. This study includes 98 patients with anterior circulation large vessel occlusion acute ischemic stroke who are eligible for bridging therapy, providing 80% power to reject the null hypothesis that, combined with fingolimod, the bridging therapy has an at least 15% higher penumbra tissue salvage index than receiving bridging therapy alone. Study outcomes The primary outcome is the penumbra tissue salvage index. Key secondary outcomes focus on: infarct growth and extent of clinical improvement from day 1 to day 7, frequency of parenchymal hemorrhage at day 1. Discussion If the hypothesis of FAMTAIS is confirmed, combination of fingolimod with bridging therapy is effective in attenuating reperfusion injury in patients with large vessel occlusion treated with 6 h of stroke onset.

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