BackgroundAccurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors.MethodsWe predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations.ResultsThe ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression.ConclusionOur machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.
Neurological diseases such as ischemic stroke and dementia are associated with compromised blood-brain barrier (BBB) permeability. Knowledge about the time course of BBB leakage may have impact on therapeutic interventions and diagnostic measures such as testing for blood biomarkers. However, reports on the timeline and pattern of this leakage are contradictory. Therefore, we aimed to assess the time course of BBB permeability in ischemic stroke patients during the first 24 hours after symptom onset using dynamic contrast enhanced (DCE) MRI at 3 Tesla. We categorized time from stroke symptom onset to imaging into the following groups 1) 0-6 hours (n=10), 2) 6-16 hours (n=14) and 3) 16-24 hours (n=29). BBB permeability differed significantly between stroke lesions and the contralesional tissue for groups 2 and 3 (p=0.006, p<0.001, Wilcoxon-signed rank test). Using univariate or multivariate linear regression analyses we found no association between BBB leakage and age, sex, hyperintense reperfusion marker (another marker of BBB permeability) hemorrhagic transformation, white matter lesion load, symptom severity, functional disability and cerebrovascular risk factors. The results of our study therefore suggest continuous BBB leakage in the first 24 hours after stroke.
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