SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

التفاصيل البيبلوغرافية
العنوان: SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy
المؤلفون: Matteo Cortinovis, Elisa Raimondi, Federico Pozzi, Alessandro Padovani, Laura Fusi, Federico Carimati, Sonia Bonacina, Mario Grassi, Valentina Mazzoleni, Alessandro Prelle, Anna Magherini, Simonetta Gerevini, Andrea Pilotto, Manuel Corato, Valeria De Giuli, Enrico Premi, Valentina Puglisi, Giorgio Silvestrelli, Francesco Santangelo, Alessia Giossi, Davide Sangalli, Maurizio Versino, Simone Beretta, Giuditta Giussani, Francesca Barbieri, Giampiero Grampa, Rubjona Xhani, Susanna Diamanti, Daria Valeria Roccatagliata, Alfonso Ciccone, Stefano Masciocchi, Elio Agostoni, Nicola Rifino, Massimo Gamba, Maria Sessa, Anna Cavallini, Sara La Gioia, Alberto Benussi, Bruno Censori, Martina Locatelli, Alessandro Pezzini, Andrea Salmaggi, Fernando Palluzzi, Mauro Magoni, Luisa Vinciguerra, Debora Pezzini, Simona Marcheselli, Carlo Ferrarese
المساهمون: Pezzini, A, Grassi, M, Silvestrelli, G, Locatelli, M, Rifino, N, Beretta, S, Gamba, M, Raimondi, E, Giussani, G, Carimati, F, Sangalli, D, Corato, M, Gerevini, S, Masciocchi, S, Cortinovis, M, La Gioia, S, Barbieri, F, Mazzoleni, V, Pezzini, D, Bonacina, S, Pilotto, A, Benussi, A, Magoni, M, Premi, E, Prelle, A, Agostoni, E, Palluzzi, F, De Giuli, V, Magherini, A, Roccatagliata, D, Vinciguerra, L, Puglisi, V, Fusi, L, Diamanti, S, Santangelo, F, Xhani, R, Pozzi, F, Grampa, G, Versino, M, Salmaggi, A, Marcheselli, S, Cavallini, A, Giossi, A, Censori, B, Ferrarese, C, Ciccone, A, Sessa, M, Padovani, A
المصدر: Journal of Neurology
سنة النشر: 2021
مصطلحات موضوعية: Male, Percentile, medicine.medical_specialty, Logistic regression, Brain Ischemia, Internal medicine, medicine, Humans, Hospital Mortality, Risk factor, Stroke, Survival analysis, Ischemic Stroke, Retrospective Studies, Original Communication, SARS-CoV-2, business.industry, Mortality rate, COVID-19, Atrial fibrillation, medicine.disease, Italy, Neurology, Risk factors, Viral infection, Etiology, Neurology (clinical), business
الوصف: Objective To characterize patients with acute ischemic stroke related to SARS-CoV-2 infection and assess the classification performance of clinical and laboratory parameters in predicting in-hospital outcome of these patients. Methods In the setting of the STROKOVID study including patients with acute ischemic stroke consecutively admitted to the ten hub hospitals in Lombardy, Italy, between March 8 and April 30, 2020, we compared clinical features of patients with confirmed infection and non-infected patients by logistic regression models and survival analysis. Then, we trained and tested a random forest (RF) binary classifier for the prediction of in-hospital death among patients with COVID-19. Results Among 1013 patients, 160 (15.8%) had SARS-CoV-2 infection. Male sex (OR 1.53; 95% CI 1.06–2.27) and atrial fibrillation (OR 1.60; 95% CI 1.05–2.43) were independently associated with COVID-19 status. Patients with COVID-19 had increased stroke severity at admission [median NIHSS score, 9 (25th to75th percentile, 13) vs 6 (25th to75th percentile, 9)] and increased risk of in-hospital death (38.1% deaths vs 7.2%; HR 3.30; 95% CI 2.17–5.02). The RF model based on six clinical and laboratory parameters exhibited high cross-validated classification accuracy (0.86) and precision (0.87), good recall (0.72) and F1-score (0.79) in predicting in-hospital death. Conclusions Ischemic strokes in COVID-19 patients have distinctive risk factor profile and etiology, increased clinical severity and higher in-hospital mortality rate compared to non-COVID-19 patients. A simple model based on clinical and routine laboratory parameters may be useful in identifying ischemic stroke patients with SARS-CoV-2 infection who are unlikely to survive the acute phase.
وصف الملف: STAMPA
اللغة: English
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8a2b27b45d0a5d5b72aff6fd654ac90
http://hdl.handle.net/11383/2121884
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....f8a2b27b45d0a5d5b72aff6fd654ac90
قاعدة البيانات: OpenAIRE