COVID-19 infection and hospitalization rate in Iranian multiple sclerosis patients: What we know by May 2021

COVID-19: The Course, Vaccination and Immune Response in People with Multiple Sclerosis: Systematic Review

When the Coronavirus Disease 2019 (COVID-19) appeared, it was unknown what impact it would have on the condition of patients with autoimmunological disorders. Attention was focused on the course of infection in patients suffering from multiple sclerosis (MS), specially treated with disease-modifying therapies (DMTs) or glucocorticoids. The impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection on the occurrence of MS relapses or pseudo-relapses was important. This review focuses on the risk, symptoms, course, and mortality of COVID-19 as well as immune response to vaccinations against COVID-19 in patients with MS (PwMS). We searched the PubMed database according to specific criteria. PwMS have the risk of infection, hospitalization, symptoms, and mortality due to COVID-19, mostly similar to the general population. The presence of comorbidities, male sex, a higher degree of disability, and older age increase the frequency and severity of the COVID-19 course in PwMS. For example, it was reported that anti-CD20 therapy is probably associated with an increased risk of severe COVID-19 outcomes. After SARS-CoV-2 infection or vaccination, MS patients acquire humoral and cellular immunity, but the degree of immune response depends on applied DMTs. Additional studies are necessary to corroborate these findings. However, indisputably, some PwMS need special attention within the context of COVID-19.

Multiple sclerosis and COVID-19: A retrospective study in Iran

OBJECTIVES

Previous studies suggested a higher rate of COVID-19 infection in patients with multiple sclerosis than in the general population, and limited studies addressed the impact of COVID-19 and its vaccination in patients with multiple sclerosis in Iran. We decided to investigate the factors associated with COVID-19 infection, the effects and side effects of the COVID-19 vaccination in patients with multiple sclerosis (MS).

METHODS

We used the data of the patients with multiple sclerosis registered in a referral clinic in Kerman, one of the large cities in Iran (a population of 537,000 inhabitants), to explore the association between demographic variables, the history of COVID-19 vaccination, and the clinical outcomes.

RESULTS

Of the 367 participants in this study, 88.3% received the COVID-19 vaccine, 35.4% were confirmed COVID-19 cases, and the incidence of COVID-19 was much higher before vaccination (24.5% before vaccination versus 10.1% after vaccination). The multivariable logistic regression model showed that male gender (OR = 2.64, 95% confidence interval: 1.21, 5.74) and current employment (OR = 3.04, 95% confidence interval: 1.59, 5.80) were associated with an increased risk of COVID-19. The only factor associated with the adverse effects of COVID-19 vaccination was the type of vaccine (AstraZeneca).

CONCLUSION

Our findings showed that the vaccination protected MS cases considerably against COVID-19. In addition, the side effects of the vaccines were not noticeably high in these cases as well. Among all COVID-19 vaccines, AstraZeneca had the most common side effects, so people must be aware of them before vaccination. The male gender and employment were the most important variables in the prevalence of COVID-19 in patients with multiple sclerosis in our study.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Introduction

The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients.

Methods

In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified.

Results

The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795).

Conclusions

Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.