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SARS-CoV-2 and stroke characteristics: a report from the Multinational COVID-19 Stroke Study Group

Authors :
Shahjouei, Shima
Tsivgoulis, Georgios
Farahmand, Ghasem
Koza, Eric
Mowla, Ashkan
Vafaei Sadr, Alireza
Kia, Arash
Vaghefi Far, Alaleh
Mondello, Stefania
Cernigliaro, Achille
Ranta, Annemarei
Punter, Martin
Khodadadi, Faezeh
Sabra, Mirna
Ramezani, Mahtab
Naderi, Soheil
Olulana, Oluwaseyi
Chaudhary, Durgesh
Lyoubi, Aicha
Campbell, Bruce
Arenillas, Juan F.
Bock, Daniel
Montaner, Joan
Aghayari Sheikh Neshin, Saeideh
Sousa, Diana Aguiar de
Tenser, Matthew S.
Aires, Ana
De Lera Alfonso, Merccedes
Alizada, Orkhan
Azevedo, Elsa
Goyal, Nitin
Babaeepour, Zabihollah
Banihashemi, Gelareh
Bonati, Leo
Cereda, Carlo
Chang, Jason J.
Crnjakovic, Miljenko
De Marchis, Gian Marco
Del Sette, Massimo
Ebrahimzadeh, Seyed Amir
Farhoudi, Mehdi
Gandoglia, Ilaria
Gonçalves, Bruno
Griessenauer, Christoph J.
Murat Hancı, Mehmet
Katsanos, Aristeides H.
Krogias, Christos
Leker, Ronen
Lotman, Lev
Mai, Jeffrey
Male, Shailesh
Malhotra, Konark
Malojcic, Branko
Mesquita, Teresa
Mir Ghasemi, Asadollah
Aref, Hany Mohamed
Mohseni Afshar, Zeinab
Moon, Jusun
Niemelä, Mika
Rezaei Jahromi, Behnam
Nolan, Lawrence
Pandhi, Abhi
Park, Jong-Ho
Pedro Marto, João
Purroy, Francisco
Ranji-Burachaloo, Sakineh
Carreira, Nuno Reis
Requena, Manuel
Rubiera del Fueyo, Marta A.
Sajedi, Seyed Aidin
Sargento-Freitas, João
Sharma, Vijay
Steiner, Thorsten
Tempro, Kristi
Turc, Guillaume
Ahmadzadeh, Yassaman
Almasi Dooghaee, Mostafa
Assarzadegan, Farhad
Babazadeh, Arefeh
Baharvahdat, Humain
Cardoso, Fabricio
Dev, Apoorva
Ghorbani, Mohammad
Hamidi, Ava
Sadat Hasheminejad, Zeynab
Hojjat-Anasri Komachali, Sahar
Khorvash, Fariborz
Kobeissy, Firas
Mirkarimi, Hamidreza
Mohammadi-Vosough, Elahe
Misra, Debdipto
Reza Noorian, Ali
Nowrouzi-Sohrabi, Peyman
Paybast, Sepideh
Poorsaadat, Leila
Roozbeh, Mehrdad
Sabayan, Behnam
Salehizadeh, Saeideh
Saberi, Alia
Sepehrnia, Mercedeh
Vahabizad, Fahimeh
Yasuda, Thomas Alexandre
Hojati Marvast, Ahmadreza
Ghabaee, Mojdeh
Rahimian, Nasrin
Harirchian, Mohammad Hossein
Borhani Haghighi, Afshin
Arora, Rohan
Ansari, Saeed
Avula, Venkatesh
Li, Jiang
Abedi, Vida
Zand, Ramin
Shahjouei, Shima
Tsivgoulis, Georgios
Farahmand, Ghasem
Koza, Eric
Mowla, Ashkan
Vafaei Sadr, Alireza
Kia, Arash
Vaghefi Far, Alaleh
Mondello, Stefania
Cernigliaro, Achille
Ranta, Annemarei
Punter, Martin
Khodadadi, Faezeh
Sabra, Mirna
Ramezani, Mahtab
Naderi, Soheil
Olulana, Oluwaseyi
Chaudhary, Durgesh
Lyoubi, Aicha
Campbell, Bruce
Arenillas, Juan F.
Bock, Daniel
Montaner, Joan
Aghayari Sheikh Neshin, Saeideh
Sousa, Diana Aguiar de
Tenser, Matthew S.
Aires, Ana
De Lera Alfonso, Merccedes
Alizada, Orkhan
Azevedo, Elsa
Goyal, Nitin
Babaeepour, Zabihollah
Banihashemi, Gelareh
Bonati, Leo
Cereda, Carlo
Chang, Jason J.
Crnjakovic, Miljenko
De Marchis, Gian Marco
Del Sette, Massimo
Ebrahimzadeh, Seyed Amir
Farhoudi, Mehdi
Gandoglia, Ilaria
Gonçalves, Bruno
Griessenauer, Christoph J.
Murat Hancı, Mehmet
Katsanos, Aristeides H.
Krogias, Christos
Leker, Ronen
Lotman, Lev
Mai, Jeffrey
Male, Shailesh
Malhotra, Konark
Malojcic, Branko
Mesquita, Teresa
Mir Ghasemi, Asadollah
Aref, Hany Mohamed
Mohseni Afshar, Zeinab
Moon, Jusun
Niemelä, Mika
Rezaei Jahromi, Behnam
Nolan, Lawrence
Pandhi, Abhi
Park, Jong-Ho
Pedro Marto, João
Purroy, Francisco
Ranji-Burachaloo, Sakineh
Carreira, Nuno Reis
Requena, Manuel
Rubiera del Fueyo, Marta A.
Sajedi, Seyed Aidin
Sargento-Freitas, João
Sharma, Vijay
Steiner, Thorsten
Tempro, Kristi
Turc, Guillaume
Ahmadzadeh, Yassaman
Almasi Dooghaee, Mostafa
Assarzadegan, Farhad
Babazadeh, Arefeh
Baharvahdat, Humain
Cardoso, Fabricio
Dev, Apoorva
Ghorbani, Mohammad
Hamidi, Ava
Sadat Hasheminejad, Zeynab
Hojjat-Anasri Komachali, Sahar
Khorvash, Fariborz
Kobeissy, Firas
Mirkarimi, Hamidreza
Mohammadi-Vosough, Elahe
Misra, Debdipto
Reza Noorian, Ali
Nowrouzi-Sohrabi, Peyman
Paybast, Sepideh
Poorsaadat, Leila
Roozbeh, Mehrdad
Sabayan, Behnam
Salehizadeh, Saeideh
Saberi, Alia
Sepehrnia, Mercedeh
Vahabizad, Fahimeh
Yasuda, Thomas Alexandre
Hojati Marvast, Ahmadreza
Ghabaee, Mojdeh
Rahimian, Nasrin
Harirchian, Mohammad Hossein
Borhani Haghighi, Afshin
Arora, Rohan
Ansari, Saeed
Avula, Venkatesh
Li, Jiang
Abedi, Vida
Zand, Ramin
Publication Year :
2020

Abstract

Background: Stroke is reported as a consequence of SARS-CoV-2 infection. However, there is a lack of regarding comprehensive stroke phenotype and characteristics Methods: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. We further investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11 [5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p<0.001) in countries with middle to high-health expenditure when compared to countries with lower health expenditure. The unsupervised machine learning identified 4 subgroups, with a relatively large group with no or limi

Details

Database :
OAIster
Notes :
application/zip, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1390672750
Document Type :
Electronic Resource