TY - JOUR
T1 - A multiparametric score for assessing the individual risk of severe Covid-19 among patients with Multiple Sclerosis
AU - Ponzano, Marta
AU - Schiavetti, Irene
AU - Bovis, Francesca
AU - Landi, Doriana
AU - Carmisciano, Luca
AU - De Rossi, Nicola
AU - Cordioli, Cinzia
AU - Moiola, Lucia
AU - Radaelli, Marta
AU - Immovilli, Paolo
AU - Capobianco, Marco
AU - Bragadin, Margherita Monti
AU - Cocco, Eleonora
AU - Scandellari, Cinzia
AU - Cavalla, Paola
AU - Pesci, Ilaria
AU - Confalonieri, Paolo
AU - Perini, Paola
AU - Bergamaschi, Roberto
AU - Inglese, Matilde
AU - Petracca, Maria
AU - Trojano, Maria
AU - Tedeschi, Gioacchino
AU - Comi, Giancarlo
AU - Battaglia, Mario Alberto
AU - Patti, Francesco
AU - Fragoso, Yara Dadalti
AU - Sen, Sedat
AU - Siva, Aksel
AU - Karabudak, Rana
AU - Efendi, Husnu
AU - Furlan, Roberto
AU - Salvetti, Marco
AU - Sormani, Maria Pia
N1 - Publisher Copyright:
© 2022
PY - 2022/7
Y1 - 2022/7
N2 - Background: Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance. Methods: Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance. Results: 3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580. Conclusions: The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).
AB - Background: Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance. Methods: Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance. Results: 3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580. Conclusions: The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).
KW - Covid-19 severity
KW - Multiple Sclerosis
KW - Risk assessment score
UR - http://www.scopus.com/inward/record.url?scp=85131413941&partnerID=8YFLogxK
U2 - 10.1016/j.msard.2022.103909
DO - 10.1016/j.msard.2022.103909
M3 - Article
C2 - 35675744
AN - SCOPUS:85131413941
SN - 2211-0348
VL - 63
JO - Multiple Sclerosis and Related Disorders
JF - Multiple Sclerosis and Related Disorders
M1 - 103909
ER -