@article{3102688,
    title = "Sars-cov-2 is a culprit for some, but not all acute ischemic strokes: A report from the multinational covid-19 stroke study group",
    author = "Shahjouei, S. and Anyaehie, M. and Koza, E. and Tsivgoulis, G. and Naderi, S. and Mowla, A. and Avula, V. and Sadr, A.V. and Chaudhary, D. and Farahmand, G. and Griessenauer, C. and Azarpazhooh, M.R. and Misra, D. and Li, J. and Abedi, V. and Zand, R. and the Multinational COVID-Stroke Study Group",
    journal = "Journal of Clinical Medicine Research",
    year = "2021",
    volume = "10",
    number = "5",
    pages = "1-14",
    publisher = "MDPI",
    issn = "1918-3003, 1918-3011",
    doi = "10.3390/jcm10050931",
    keywords = "adult;  aged;  Article;  atrial fibrillation;  brain atherosclerosis;  brain ischemia;  cardioembolic stroke;  cohort analysis;  comorbidity assessment;  coronavirus disease 2019;  female;  human;  ischemic heart disease;  k means clustering;  learning algorithm;  major clinical study;  male;  multicenter study;  neoplasm;  neuroimaging;  observational study;  prospective study;  risk assessment;  stroke patient;  unsupervised machine learning",
    abstract = "Background. SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investi-gated whether the comorbidities and their frequencies under each subclass of TOAST criteria were similar to the AIS population studies prior to the pandemic. Methods. This is a report from the Multinational COVID-19 Stroke Study Group. We present an original dataset of SASR-CoV-2 infected patients who had a subsequent stroke recorded through our multicenter prospective study. In addi-tion, we built a dataset of previously reported patients by conducting a systematic literature review. We demonstrated distinct subgroups by clinical risk scoring models and unsupervised machine learning algorithms, including hierarchical K-Means (ML-K) and Spectral clustering (ML-S). Results. This study included 323 AIS patients from 71 centers in 17 countries from the original dataset and 145 patients reported in the literature. The unsupervised clustering methods suggest a distinct cohort of patients (ML-K: 36% and ML-S: 42%) with no or few comorbidities. These patients were more than 6 years younger than other subgroups and more likely were men (ML-K: 59% and ML-S: 60%). The majority of patients in this subgroup suffered from an embolic-appearing stroke on imaging (ML-K: 83% and ML-S: 85%) and had about 50% risk of large vessel occlusions (ML-K: 50% and ML-S: 53%). In addition, there were two cohorts of patients with large-artery atherosclerosis (ML-K: 30% and ML-S: 43% of patients) and cardioembolic strokes (ML-K: 34% and ML-S: 15%) with consistent comorbidity and imaging patterns. Binominal logistic regression demonstrated that ischemic heart disease (odds ratio (OR), 4.9; 95% confidence interval (CI), 1.6–14.7), atrial fibrillation (OR, 14.0; 95% CI, 4.8–40.8), and active neoplasm (OR, 7.1; 95% CI, 1.4–36.2) were associated with cardioembolic stroke. Conclusions. Although a cohort of young and healthy men with cardioembolic and large vessel occlusions can be distinguished using both clinical sub-grouping and unsupervised clustering, stroke in other patients may be explained based on the existing comorbidities. © 2021 by the authors. Licensee MDPI, Basel, Switzerland."
}