key: cord-0897076-oev6q7ua
authors: Etemadifar, Masoud; Nouri, Hosein; Maracy, Mohammad Reza; Sigari, Amirhosein Akhavan; Salari, Mehri; Blanco, Yolanda; Sepúlveda, Maria; Zabalza, Ana; Mahdavi, Sepide; Baratian, Mahshid; Sedaghat, Nahad
title: Risk Factors of Severe COVID-19 in Multiple Sclerosis Patients: A Systematic Review and Meta-Analysis
date: 2021-11-04
journal: Rev Neurol (Paris)
DOI: 10.1016/j.neurol.2021.10.003
sha: 936196d248f90d9a742818929b9c8d440ab25fec
doc_id: 897076
cord_uid: oev6q7ua

Objectives: To gather, synthesize, and meta-analyze data regarding the risk factors associated with a severe course of COVID-19 among patients with multiple sclerosis (pwMS). Methods: MEDLINE, Embase, Scopus, and WoS were searched in May 2021. Briefly, the eligibility criteria included: 1) studies assessing COVID-19 severity among adult pwMS, 2) definitive diagnoses or high clinical suspicion of COVID-19, 3) a categorization of COVID-19 severity into at least two categories, 4) quantitative effect size and precision measurements, and 5) English language; and 6) clear effect size/precision measures. internal validity of studies was assessed using the NIH Quality Assessment Tools. A list of possible risk factors was created based on the search results and was later used in extraction, synthesis, and meta-analysis of the data. Results: Thirteen studies were included in the syntheses. Outcome measures were either extracted from the papers, obtained from the primary researchers or calculated manually. The meta-analyses showed a significantly (P < 0.05) increased odds of a severe COVID-19 in pwMS with all of the assessed risk factors, except smoking and most DMTs. Conclusion: This study facilitates evidence-based risk/benefit assessments in practice. Older male progressive MS patients on anti-CD20 therapies are more at risk of an unfortunate COVID-19 outcome.

1. Introduction: 1 Since the beginning of the COVID-19 pandemic, major concerns were raised among the patients with 2

Multiple Sclerosis (pwMS) and their physicians, considering their disabilities, their chronic states of 3 immunosuppression, and their higher susceptibility to infections and their unfortunate outcomes [1] . 4

Therefore, ever since, a lot of research has been aiming at investigating the possible relationships between 5 COVID-19 and Multiple Sclerosis (MS). Identification of the risk factors, which render pwMS more 6 susceptible to worse COVID-19 outcomes, has been among the aims of these studies. Results however, 7

have been contrary, as expected from primary observational investigations. Managing pwMS during the 8 pandemic, requires careful evidence-based assessments of individual risk/benefit profiles. Therefore, we 9

aimed to gather and synthesize the published evidence concerning the risk factors reportedly associated 10 with a more severe course of COVID-19, in a systematic review of literature, followed by a meta-analysis. 11 2. Methods 12 2.1 Framework, Search Strategy and Eligibility Criteria 13 The PRISMA guidelines were followed in the conduction of this study. Initially, a comprehensive search 14 of MEDLINE, Embase, Scopus and Web of Science was conducted by two independent reviewers (SM and 15 MB, the search team) in May 2021, using the terms: (coronavirus disease 2019 OR COVID-19 OR SARS-16

CoV-2 OR COVID) AND (Multiple Sclerosis). The search team used the Mendeley application to detect, 17 scan, and remove duplicates. The same application was used to identify the reviews, case reports, and 18 commentaries, which were excluded after confirmation. The remaining studies were scanned for relevant 19 titles/abstracts. This process was conducted two separate times by two independent reviewers. The studies 20 that at least one reviewer from the search team considered relevant, were sought for retrieval. 21 22

The inclusion criteria were pre-defined as: 1) observational studies assessing COVID-19 severity among 23 adult (>18 years old) MS patients, 2) laboratory-or radiology-based diagnoses or high clinical suspicion of 24 COVID-19, 3) a categorization of COVID-19 severity into at least two clearly defined categories (e.g., 25 mild, severe, or similar terminologies), 4) estimation of the outcome of possible pre-defined risk factors, 26

using proper quantitative effect size and precision measurements, and 5) studies published in the English 27 language. Exclusion criteria included: 1) unclear effect size and/or precision measures. Full texts of the 28 relevant papers were assessed for eligibility and quality independently by two reviewers (AAS and HN, the 29 methodology team). As the studies used different variables to measure the effect size of each risk factor, the most abundant 42 effect measure among all, for each risk factor, was considered the primary effect measure and was used in 43 the syntheses. We tried to minimize the exclusion of data from studies that used different variables to 44 measure the effect size of each risk factor and contacted the primary investigators of those studies. They 45 were asked to calculate and share the required effect measures or share their raw data, enabling us to do so. 46

If the primary investigators failed to share the required measures, the data extraction team (NS and MRM) 47 aimed to manually calculate the effect measures from the descriptive data presented in their paper. Finally, 48

if the required effect measures could not be obtained, either way, the study was excluded from synthesis 49 regarding the particular risk factors. Studies also differed in the method of effect measure calculations (e.g., 50

accounting for different confounding factors). This was regarded as a source of possible inter-study 51

heterogeneity. In the case of a study estimating a specific effect size via different statistical methods, the 52 result from the method accounting for the most confounding factors was extracted and used in the synthesis. 53

The final list of risk factors and their extracted effect and precision measures can be interpreted from table 54

1. 55 

Forest plots were used to display the results of syntheses visually. Based on the inter-study heterogeneity 59 regarding each outcome, determined by Cochran's Q and I-squared tests, fixed-or random-effects models 60

were used for meta-analysis of homo-and heterogenous results, respectively. More specifically, a random-61 effects model was used in the case of I 2 > 50%. In case of a significant inter-study heterogeneity (I 2 > 50%), 62 a meta-regression analysis was utilized to assess the inter-study difference in analysis methods as a possible 63 source of heterogeneity. Sensitivity analyses included performing meta-analysis only on studies with good 64 quality based on NIH Quality Assessment Tools [2]. Egger's test and visual asymmetry of funnel plots were 65 used to assess the risk of bias due to missing results (reporting/publication bias) in the syntheses. Statistical 66 significance was defined as P-value < 0.05. The certainty of evidence was rated using the GRADE 67 approach, by three independent reviewers (NS, ME, and MRM). The STATA14 software for macOS was 68 used to gather, synthesize, and analyze the data. 69 70

An approval from ethics committee was not required to conduct this study, as it was not a primary 72 investigation on individuals. Before initiation, the protocol of the study was approved and registered by the The PRISMA flow diagram, describing the study selection processes, from the search to the final inclusion 81 in syntheses, can be interpreted from figure 1. Thirteen studies were included in the final syntheses based 82 on the eligibility criteria. Two studies [3, 4] were excluded despite meeting the eligibility criteria, because 83 no data could be extracted from them in any way. General information pertaining to each included study 84 and the extracted results from each can be interpreted from table 2. The reviewers did not detect any sign 85 of duplicate data among the included studies, although its possibility should be acknowledged, considering 86 the large extents of global and regional shared data registries and databases. 87 confounding variables in its analyses, which was regarded as a source of bias/heterogeneity. Studies 94 differed in their methodology, some being registry-based, while others being questionnaire-and follow-up-95

based. Some outcomes required for syntheses were not presented in most of primary studies, forcing us to 96 contact the primary researchers, of which the majority could not share the measures apart from the ones 97 presented in their papers. Therefore, some of the required outcomes were calculated manually using 98 univariate analysis and others were excluded from syntheses. This should be regarded as a source of 99 possible bias, which was investigated in the sensitivity analyses, by performing no manual calculations and 100 extracting all the outcome measures from the studies with good quality based on NIH Quality Assessment 101

Tools [2] . Furthermore, most studies were conducted with the primary goal of investigating the contraction 102 of COVID-19 rather than investigating its different courses among pwMS. This led to limited numbers of COVID-19-contracted pwMS being a subgroup in the study samples and therefore, may have presented 104 possible biases due to limited sample sizes and analytic power. This problem might have been amplified by 105

using the random-effects model regarding the risk factors that showed inter-study heterogenous results. 106

This was also investigated via the sensitivity analyses, in which the meta-analyses were performed on the 107 studies with sufficient sample sizes and analytic powers. The results of bias assessments in each individual 108 study, using NIH Quality Assessment Tools [2], can be retrieved from the supplementary material. 109 110

The final results of syntheses and meta-analyses are summarized in table 3 This study provided robust evidence regarding the risk factors of a severe COVID-19 among pwMS, 133 guiding clinicians to better assess individualized risk profiles and better manage each patient in clinical 134

practice. Among the studied risk factors, a progressive phenotype of MS seems to multiply the odds of a 135

severe course of COVID-19, by roughly four times, which is an alarming number. Therefore, close 136 observations of these patients and raising awareness among themselves and their relatives to take the 137 protective measures seriously, may be of more importance than the general population or other pwMS. 138

Also, prioritizing their vaccination against COVID-19, advising them of receiving booster doses and the 139 continuation of protective measures, even after being vaccinated, may be reasonable. To a lesser extent, 140 this also accounts for patients with other risk factors as well. 141 142

Most DMTs did not show to be associated with a severe COVID-19, and therefore, they can be continued 143 with caution throughout the pandemic; with anti-CD20 drugs, however, the issue is slightly different. Anti-144 CD20 therapies were significantly associated with a severe course of COVID-19 in our meta-analysis, but 145 whether or not anti-CD20 treatments should be halted or replaced by other therapies is controversial, and 146 more practical approaches may be more appreciated [18] . Overall, when it comes to recovery from the 147 infection, some suggest that B cells may not be the sole requirement of the host's immune system, with 148 innate and cellular immune components exerting their antiviral effects as planned [19] . Currently, high-149 quality data on long-term immunity against SARS-CoV-2 in pwMS treated with anti-CD20 drugs is lacking. 150

The matter gains more importance considering the controversial data on post-vaccination cellular responses 151 [20, 21] and blunted humoral responses [21-24] in these patients. As supported by theory, the reports of 152 Sormani et al. [22] and Stefanski et al. [23] showed delaying doses of anti-CD20 therapies to be associated 153 with more favorable post-vaccination humoral responsesa key point to keep in mind when developing 154 vaccination strategies for pwMS on anti-CD20 therapies, who were shown to be at higher risk of 155 unfavorable COVID-19 outcomes. 156 157

In the same line, natalizumab is used as a second-line therapy, and treated patients are likely to have a 158

higher EDSS when treatment is started. In fact, natalizumab therapy associates with a small increased risk 159 of upper respiratory tract infections [25] , and theoretically, due to its mechanism of action, natalizumab 160 could be effective in reducing lymphocyte trafficking to the lung and mucosa as much as possible from all over the globe, and utilizing statistical tools (e.g., meta-regression) to account 192 for different confounders and sources of inter-study heterogeneity. Nevertheless, this problem has been an 193 issue over the years and still remains to be discussed. In this study, aiming to reduce selection bias, we 194 approached the issue by including as much studies as we could regardless of their locations, methods of 195 analysis, and overall heterogeneity compared to other studies, including in the syntheses the results from 196 all over the globe and from both univariate and multivariable analyses, and thereafter, utilized meta-197 regression to investigate the possible source of inter-study heterogeneity. An alternative approach (which a 198 lot of statisticians would not approve), is to scan the different studies, exclude the heterogenous ones, and 199 synthesize/meta-analyze the most homogenous. 200 201

Nevertheless, there is still a need for more evidence to estimate the effects of mentioned risk factors more 202

precisely on the course of COVID-19. Future studies can also focus on the mechanistic processes which 203 render pwMS with risk factors more susceptible to COVID-19. Unfortunately, discussions in this regard do 204 not fit the present paper. 205

Most of the limitations in each step of this study, and the methods used to tackle them were mentioned in 207 the methods and results section. One of the unmentioned limitations of this study, might be the two-levels 208 outcome measurement. The two-level outcome measurement based on hospitalization was considered in 209 order to facilitate the data extraction processes, although it might have presented uncertainty to the results. 210

For instance, different countries have followed different guidelines for hospitalizing patients and it could 211 not be determined if the hospitalized pwMS all experienced similar disease courses, a problem that was not 212 investigated in this study and might have led to over-or underrepresentation of the results. Also, a two-213 member search team and limited searched databases still leaves a chance of missing relevant studies and 214

differences of COVID-19 situations and managements in 215 different regions) could have been investigated in meta-regression. Nevertheless, this was a meta-analysis 216 of observational studies and therefore

We would like to thank Dr. Narges Motamedi, Dr. Maria Pia Sormani, and Dr. Irene Schiavetti for their 220 valuable consultations regarding statistical methodologies. 221 222 7. Funding and Competing Interests

Dr Yolanda Blanco has received 225 speaker honoraria from Novartis, Roche, Genzyme-Sanofi and Biogen. Dr Ana Zabalza has received travel 226 expenses for scientific meetings from Biogen-Idec and Novartis, speaking honoraria from Eisai and a study 227 grant from Novartis. The other authors of this study report no relative competing interests

Infection Risks Among 231 Patients With Multiple Sclerosis Treated With Fingolimod, Natalizumab, Rituximab, and Injectable 232 Therapies

NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional 234 Studies Systematic Review

Immunosuppression in 236 chronic autoimmune neurological disorders during the COVID-19 pandemic

Incidence and clinical outcome of Coronavirus disease 2019 in a cohort of 11,560 Brazilian 239 patients with multiple sclerosis

Associated With SARS-CoV-2 Infection in a North American Registry of Patients With Multiple 242 Sclerosis

Disease-Modifying 244 Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

COVID-19 in ocrelizumab-247 treated people with multiple sclerosis

Clinical Characteristics 249 and Outcomes in Patients With Coronavirus Disease 2019 and Multiple Sclerosis

COVID-19 in Patients 252 with Multiple Sclerosis: Associations with Disease-Modifying Therapies

The outcome of a national 255 MS-Covid-19 study: What the Turkish MS cohort reveals?

COVID-19 in multiple 257 sclerosis and neuromyelitis optica spectrum disorder patients in Latin America: COVID-19 in MS 258 and NMOSD patients in LATAM

COVID-19 in multiple sclerosis patients: susceptibility, severity risk factors and serological 261 response

COVID-19 in multiple sclerosis: 263 The Dutch experience

COVID-19 outcomes in MS: 265 Observational study of early experience from NYU Multiple Sclerosis Comprehensive Care Center

Evaluation of the rate of COVID-19 268 infection, hospitalization and death among Iranian patients with multiple sclerosis

Incidence 271 and Impact of COVID-19 in MS: A Survey From a Barcelona MS Unit

COVID-19 in multiple sclerosis 274 patients and risk factors for severe infection

COVID-19 vaccine-readiness for 276 anti-CD20-depleting therapy in autoimmune diseases

The underpinning biology relating to 278 multiple sclerosis disease modifying treatments during the COVID-19 pandemic

Patients treated with anti-281 CD20 therapy can mount robust T cell responses to mRNA-based COVID-19 vaccines

Humoral and cellular 284 responses to mRNA vaccines against SARS-CoV2 in patients with a history of CD20-B-cell depleting 285 therapy

Effect of SARS-CoV-287 2 mRNA Vaccination in MS Patients Treated With Disease Modifying Therapies. SSRN

B cell 289 numbers predict humoral and cellular response upon SARS-CoV-2 vaccination among patients 290 treated with rituximab

COVID-19 vaccine 292 response in people with multiple sclerosis

Effect of natalizumab on disease 294 progression in secondary progressive multiple sclerosis (ASCEND): a phase 3, randomised, double-295 blind, placebo-controlled trial with an open-label extension

Cell Adhesion Antagonists

Risk factors for Coronavirus 299 Disease 2019 (COVID-19) severity and mortality among solid cancer patients and impact of the 300 disease on anticancer treatment: A French nationwide cohort study

An interactive web-based dashboard to track COVID-19 in real time. The 303 Lancet infectious diseases 2020