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

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 a...

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Detalles Bibliográficos
Autores principales: Pezzini, Alessandro, Grassi, Mario, Silvestrelli, Giorgio, Locatelli, Martina, Rifino, Nicola, Beretta, Simone, Gamba, Massimo, Raimondi, Elisa, Giussani, Giuditta, Carimati, Federico, Sangalli, Davide, Corato, Manuel, Gerevini, Simonetta, Masciocchi, Stefano, Cortinovis, Matteo, La Gioia, Sara, Barbieri, Francesca, Mazzoleni, Valentina, Pezzini, Debora, Bonacina, Sonia, Pilotto, Andrea, Benussi, Alberto, Magoni, Mauro, Premi, Enrico, Prelle, Alessandro Cesare, Agostoni, Elio Clemente, Palluzzi, Fernando, De Giuli, Valeria, Magherini, Anna, Roccatagliata, Daria Valeria, Vinciguerra, Luisa, Puglisi, Valentina, Fusi, Laura, Diamanti, Susanna, Santangelo, Francesco, Xhani, Rubjona, Pozzi, Federico, Grampa, Giampiero, Versino, Maurizio, Salmaggi, Andrea, Marcheselli, Simona, Cavallini, Anna, Giossi, Alessia, Censori, Bruno, Ferrarese, Carlo, Ciccone, Alfonso, Sessa, Maria, Padovani, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142879/
https://www.ncbi.nlm.nih.gov/pubmed/34031747
http://dx.doi.org/10.1007/s00415-021-10620-8
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-021-10620-8.