Dataset Information

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

ABSTRACT: 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. 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. 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. ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

SUBMITTER: Park D

PROVIDER: S-EPMC8534707 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

Park Dougho D Jeong Eunhwan E Kim Haejong H Pyun Hae Wook HW Kim Haemin H Choi Yeon-Ju YJ Kim Youngsoo Y Jin Suntak S Hong Daeyoung D Lee Dong Woo DW Lee Su Yun SY Kim Mun-Chul MC

Diagnostics (Basel, Switzerland) 20211015 10

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 34679606

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Project description:Background and purposeMechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation.Materials and methodsA two-center retrospective cohort of 293 patients with AIS who underwent thrombectomy was analyzed. ML models were developed to predict dichotomized modified Rankin score at 90 days (mRS-90) using clinical and imaging features, both separately and combined. Conventional and experimental imaging biomarkers were quantified using automated image-processing software from non-contract computed tomography (CT) and computed tomography angiography (CTA). Shapley Additive Explanation (SHAP) was applied for model interpretability and predictor importance analysis of the optimal model.ResultsMerging clinical and imaging features returned the best results for mRS-90 prediction. The best performing classifier was Extreme Gradient Boosting (XGB) with an area under the receiver operating characteristic curve (AUC) = 84% using selected features. The most important classifying features were age, baseline National Institutes of Health Stroke Scale (NIHSS), occlusion side, degree of brain atrophy [primarily represented by cortical cerebrospinal fluid (CSF) volume and lateral ventricle volume], early ischemic core [primarily represented by e-Alberta Stroke Program Early CT Score (ASPECTS)], and collateral circulation deficit volume on CTA.ConclusionMachine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.

Project description:BackgroundAlthough adiponectin is a major adipocytokine that affects the pathogenesis of various cardiovascular diseases, its clinical significance in stroke remains controversial. The purpose of this study was to assess the impact of serum adiponectin levels on functional prognosis in patients with ischemic stroke.MethodsThis was a prospective, observational cohort study. Consecutive first-ever ischemic stroke patients without any pre-morbid handicap admitted to our hospital were identified from December 2017 to December 2018. Serum concentration of adiponectin was routinely measured within the first 24 h after admission by a commercially available sandwich ELISA. Associations between adiponectin and either clinical severity at admission, poor outcomes or mortality at 3-month after admission were analyzed using logistic regression to obtain odds ratios (OR) and 95% confidence intervals (CI).ResultsThe serum level of adiponectin was obtained in 227 patients with a median value of 7.0 μg/ml, which was significantly higher (P < 0.001) than in those heathy control. Adiponectin levels were associated with moderate-to-high stroke, and risk increased by 12% (OR = 1.12; 95% CI 1.03-1.25; P = 0.002). Patients with a poor outcome and nonsurvivors had significantly increased adiponectin levels on admission (P < 0.001, all). In multivariate logistic regression analysis, adiponectin was an independent predictor of functional outcome and mortality, and risk increased by 24% (OR = 1.24, 95% CI 1.13-1.37; P < 0.001) and 31% (1.31 [1.18-1.46], P < 0.001), respectively. Kaplan-Meier analysis suggested that the patients with high serum adiponectin levels had a higher risk of death than those patients with low levels (log-rank test P < 0.001).ConclusionsOur results show that high adiponectin is associated with stroke severity and support the hypothesis that adiponectin can be serve as a biomarker of poor outcome after stroke, independent of baseline variables. Trial registration ChiCTR-OPC-17013501. Retrospectively Registered 21 September 2017.

Project description:Objective: Mechanical retrieval of thrombotic material from acute ischemic stroke patients provides a unique entry point for translational research investigations. Here, we resolved the proteomes of cardioembolic and atherothrombotic cerebrovascular human thrombi and applied an artificial intelligence routine to examine protein signatures between the two selected groups. Methods: We specifically used n = 32 cardioembolic and n = 28 atherothrombotic diagnosed thrombi from patients suffering from acute stroke and treated by mechanical thrombectomy. Thrombi proteins were successfully separated by gel-electrophoresis. For each thrombi, peptide samples were analyzed by nano-flow liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) to obtain specific proteomes. Relative protein quantification was performed using a label-free LFQ algorithm and all dataset were analyzed using a support-vector-machine (SVM) learning method. Data are available via ProteomeXchange with identifier PXD020398. Clinical data were also analyzed using SVM, alone or in combination with the proteomes. Results: A total of 2,455 proteins were identified by nano-LC-MS/MS in the samples analyzed, with 438 proteins constantly detected in all samples. SVM analysis of LFQ proteomic data delivered combinations of three proteins achieving a maximum of 88.3% for correct classification of the cardioembolic and atherothrombotic samples in our cohort. The coagulation factor XIII appeared in all of the SVM protein trios, associating with cardioembolic thrombi. A combined SVM analysis of the LFQ proteome and clinical data did not deliver a better discriminatory score as compared to the proteome only. Conclusion: Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.

Dataset Information

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

Publications

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

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