Journal Article

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

Padovani Alessandro, Magherini, Anna, Masciocchi Stefano, Rubjona, Xhani, Raimondi, Elisa, Puglisi Valentina, Locatelli Martina, Cortinovis Matteo, Sessa, Maria, Gamba Massimo, Barbieri, Francesca, Premi Enrico, Censori Bruno, Rifino Nicola, Corato Manuel, Cavallini, Anna, Diamanti Susanna, Prelle Alessandro Cesare, Ciccone, Alfonso, Roccatagliata, Daria Valeria, Grampa Giampiero, Vinciguerra Luisa, Versino Maurizio, Beretta, Simone, Mazzoleni Valentina, De Giuli Valeria, Bonacina, Sonia, Santangelo, Francesco, Fusi, Laura, Marcheselli Simona, Ferrarese, Carlo, Sangalli Davide, Benussi, Alberto, Pilotto, Andrea, Giossi Alessia, Magoni Mauro, Giussani Giuditta, Gerevini Simonetta, Palluzzi Fernando, Grassi, Mario, Pezzini Debora, Carimati Federico, Agostoni, Elio Clemente, Pozzi Federico, Salmaggi, Andrea, Pezzini Alessandro, Silvestrelli Giorgio, La Gioia Sara

2022

Overview

ObjectiveTo 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.MethodsIn 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.ResultsAmong 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.ConclusionsIschemic 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.

• Publisher: Springer Nature B.V
• Subject: Classification / Coronaviruses / COVID-19 / Death / Etiology / Fibrillation / Infections / Ischemia / Laboratories / Neurology / Patients / Regression analysis / Risk factors / Severe acute respiratory syndrome coronavirus 2 / Stroke / Survival analysis