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

• DOI: 10.1007/s00415-021-10620-8
• Publication Date: 2021-05-24T16:02:51Z
• Authors: Alessandro Pezzini; Mario Grassi; Giorgio Silvestrelli; Martina Locatelli; Nicola Rifino; Simone Beretta; Massimo Gamba; Elisa Raimondi; Giuditta Giussani; Federico Carimati; Davide Sangalli; Manuel Corato; Simonetta Gerevini; Stefano Masciocchi; Matteo Cortinovis; Sara La Gioia; Francesca Barbieri; Valentina Mazzoleni; Debora Pezzini; Sonia Bonacina; Andrea Pilotto; Alberto Benussi; Mauro Magoni; Enrico Premi; Alessandro Cesare...; Elio Clemente Ago...; Fernando Palluzzi; Valeria De Giuli; Anna Magherini; Daria Valeria Roc...; Luisa Vinciguerra; Valentina Puglisi; Laura Fusi; Susanna Diamanti; Francesco Santangelo; Rubjona Xhani; Federico Pozzi; Giampiero Grampa; Maurizio Versino; Andrea Salmaggi; Simona Marcheselli; Anna Cavallini; Alessia Giossi; Bruno Censori; Carlo Ferrarese; Alfonso Ciccone; Maria Sessa; Alessandro Padovani
• Keywords: Male; Original Communication; SARS-CoV-2; 610; COVID-19; COVID-19; Risk factors; Stroke; Viral infection;; Brain Ischemia; 3. Good health; Stroke; 03 medical and health sciences; 0302 clinical medicine; Italy; Viral infection; Risk Factors; Humans; COVID-19; Risk factors; Stroke; Viral infection; Risk factor; Hospital Mortality; Ischemic Stroke; Retrospective Studies

Abstract 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.