Predictive factors for a severe course of COVID-19 infection in myasthenia gravis patients with an overall impact on myasthenic outcome status and survival

Myasthenia Gravis and COVID-19: A Systematic Review and Meta-analysis

Introduction

Patients with myasthenia gravis (MG), an autoimmune disease affecting the neuromuscular junction, exhibits varying rates of COVID-19 infection across different studies. This systematic review and meta-analysis aim to estimate the pooled prevalence of COVID-19 infection in individuals with MG.

Methods

We systematically searched PubMed, Scopus, EMBASE, Web of Science, Google Scholar, and gray literature, including references to the research published before October 2021. The total number of participants, the first author, the publication year, the country of origin, the number of MG patients, their symptoms, hospitalization rates, and deaths were all extracted as study data.

Results

Our literature search yielded 253 articles, of which 75 remained after removing duplicates. Finally, 18 articles were included in the meta-analysis. The pooled prevalence of COVID-19 infection in MG cases was found to be 2% (95% CI, 1%, 3%; I2=85%, P<0.001). Additionally, the pooled prevalence of hospitalization among those with COVID-19 infection was 43% (95% CI, 26%, 60%; I2=97.6%; P<0.001), and the pooled prevalence of MG exacerbation was 33% (95% CI, 20%, 46%; I2=92.6%; P<0.001).

Conclusion

In summary, this systematic review and meta-analysis reveal that the pooled prevalence of COVID-19 infection in individuals with MG is 2%.

A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones

Introduction

We conducted a 3-month, prospective study in a population of patients with Myasthenia Gravis (MG), utilizing a fully decentralized approach for recruitment and monitoring (ClinicalTrials.gov Identifier: NCT04590716). The study objectives were to assess the feasibility of collecting real-world data through a smartphone-based research platform, in order to characterize symptom involvement during MG exacerbations.

Methods

Primary data collection included daily electronically recorded patient-reported outcomes (ePROs) on the presence of MG symptoms, the level of symptom severity (using the MG-Activities of Daily Living assessment, MG-ADL), and exacerbation status. Participants were also given the option to contribute data on their physical activity levels from their own wearable devices.

Results

The study enrolled and onboarded 113 participants across 37 US states, and 73% (N= 82) completed the study. The mean age of participants was 53.6 years, 60% were female. Participants were representative of a moderate to severe MG phenotype, with frequent exacerbations, high symptom burden and multiple comorbidities. 55% of participants (N=45) reported MG exacerbations during the study, with an average of 6.3 exacerbation days per participant. Median average MG-ADL scores for participants during self-reported exacerbation and non-exacerbation periods were 7 (interquartile range 4-9, range 1-19) and 0.3 (interquartile range 0-0.8, range 0-9), respectively. Analyses examining relationships between patient-reported and patient-generated health data streams and exacerbation status demonstrated concordance between self-reported MG-ADL scores and exacerbation status, and identified features that may be used to understand and predict the onset of MG symptom exacerbations, including: 1.) dynamic changes in day-to-day symptom reporting and severity 2.) daily step counts as a measure of physical activity and 3.) clinical characteristics of the patient, including the amount of time since their initial diagnosis and their active medications related to MG treatment. Finally, application of unsupervised machine learning methods identified unique clusters of exacerbation subtypes, each with their own specific representation of symptoms and symptom severity.

Conclusion

While these symptom signatures require further study and validation, our results suggest that digital phenotyping, characterized by increased multidimensionality and frequency of the data collection, holds promise for furthering our understanding of clinically significant exacerbations and reimagining the approach to treating MG as a heterogeneous condition.